Essential basic functionality

Here we discuss a lot of the essential functionality common to the pandas data structures. Here’s how to create some of the objects used in the examples from the previous section:

In [1]: index = pd.date_range('1/1/2000', periods=8)

In [2]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

In [3]: df = pd.DataFrame(np.random.randn(8, 3), index=index,
   ...:                   columns=['A', 'B', 'C'])
   ...: 

Head and tail

To view a small sample of a Series or DataFrame object, use the head() and tail() methods. The default number of elements to display is five, but you may pass a custom number.

In [4]: long_series = pd.Series(np.random.randn(1000))

In [5]: long_series.head()
Out[5]: 
0   -1.157892
1   -1.344312
2    0.844885
3    1.075770
4   -0.109050
dtype: float64

In [6]: long_series.tail(3)
Out[6]: 
997   -0.289388
998   -1.020544
999    0.589993
dtype: float64

Attributes and underlying data

pandas objects have a number of attributes enabling you to access the metadata

  • shape: gives the axis dimensions of the object, consistent with ndarray

  • Axis labels
    • Series: index (only axis)

    • DataFrame: index (rows) and columns

Note, these attributes can be safely assigned to!

In [7]: df[:2]
Out[7]: 
                   A         B         C
2000-01-01 -0.173215  0.119209 -1.044236
2000-01-02 -0.861849 -2.104569 -0.494929

In [8]: df.columns = [x.lower() for x in df.columns]

In [9]: df
Out[9]: 
                   a         b         c
2000-01-01 -0.173215  0.119209 -1.044236
2000-01-02 -0.861849 -2.104569 -0.494929
2000-01-03  1.071804  0.721555 -0.706771
2000-01-04 -1.039575  0.271860 -0.424972
2000-01-05  0.567020  0.276232 -1.087401
2000-01-06 -0.673690  0.113648 -1.478427
2000-01-07  0.524988  0.404705  0.577046
2000-01-08 -1.715002 -1.039268 -0.370647

Pandas objects (Index, Series, DataFrame) can be thought of as containers for arrays, which hold the actual data and do the actual computation. For many types, the underlying array is a numpy.ndarray. However, pandas and 3rd party libraries may extend NumPy’s type system to add support for custom arrays (see dtypes).

To get the actual data inside a Index or Series, use the .array property

In [10]: s.array
Out[10]: 
<PandasArray>
[ 0.4691122999071863, -0.2828633443286633, -1.5090585031735124,
 -1.1356323710171934,  1.2121120250208506]
Length: 5, dtype: float64

In [11]: s.index.array
Out[11]: 
<PandasArray>
['a', 'b', 'c', 'd', 'e']
Length: 5, dtype: object

array will always be an ExtensionArray. The exact details of what an ExtensionArray is and why pandas uses them is a bit beyond the scope of this introduction. See dtypes for more.

If you know you need a NumPy array, use to_numpy() or numpy.asarray().

In [12]: s.to_numpy()
Out[12]: array([ 0.4691, -0.2829, -1.5091, -1.1356,  1.2121])

In [13]: np.asarray(s)
Out[13]: array([ 0.4691, -0.2829, -1.5091, -1.1356,  1.2121])

When the Series or Index is backed by an ExtensionArray, to_numpy() may involve copying data and coercing values. See dtypes for more.

to_numpy() gives some control over the dtype of the resulting numpy.ndarray. For example, consider datetimes with timezones. NumPy doesn’t have a dtype to represent timezone-aware datetimes, so there are two possibly useful representations:

  1. An object-dtype numpy.ndarray with Timestamp objects, each with the correct tz

  2. A datetime64[ns] -dtype numpy.ndarray, where the values have been converted to UTC and the timezone discarded

Timezones may be preserved with dtype=object

In [14]: ser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))

In [15]: ser.to_numpy(dtype=object)
Out[15]: 
array([Timestamp('2000-01-01 00:00:00+0100', tz='CET', freq='D'),
       Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')],
      dtype=object)

Or thrown away with dtype='datetime64[ns]'

In [16]: ser.to_numpy(dtype="datetime64[ns]")
Out[16]: 
array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00.000000000'],
      dtype='datetime64[ns]')

Getting the “raw data” inside a DataFrame is possibly a bit more complex. When your DataFrame only has a single data type for all the columns, DataFrame.to_numpy() will return the underlying data:

In [17]: df.to_numpy()
Out[17]: 
array([[-0.1732,  0.1192, -1.0442],
       [-0.8618, -2.1046, -0.4949],
       [ 1.0718,  0.7216, -0.7068],
       [-1.0396,  0.2719, -0.425 ],
       [ 0.567 ,  0.2762, -1.0874],
       [-0.6737,  0.1136, -1.4784],
       [ 0.525 ,  0.4047,  0.577 ],
       [-1.715 , -1.0393, -0.3706]])

If a DataFrame contains homogeneously-typed data, the ndarray can actually be modified in-place, and the changes will be reflected in the data structure. For heterogeneous data (e.g. some of the DataFrame’s columns are not all the same dtype), this will not be the case. The values attribute itself, unlike the axis labels, cannot be assigned to.

Note

When working with heterogeneous data, the dtype of the resulting ndarray will be chosen to accommodate all of the data involved. For example, if strings are involved, the result will be of object dtype. If there are only floats and integers, the resulting array will be of float dtype.

In the past, pandas recommended Series.values or DataFrame.values for extracting the data from a Series or DataFrame. You’ll still find references to these in old code bases and online. Going forward, we recommend avoiding .values and using .array or .to_numpy(). .values has the following drawbacks:

  1. When your Series contains an extension type, it’s unclear whether Series.values returns a NumPy array or the extension array. Series.array will always return an ExtensionArray, and will never copy data. Series.to_numpy() will always return a NumPy array, potentially at the cost of copying / coercing values.

  2. When your DataFrame contains a mixture of data types, DataFrame.values may involve copying data and coercing values to a common dtype, a relatively expensive operation. DataFrame.to_numpy(), being a method, makes it clearer that the returned NumPy array may not be a view on the same data in the DataFrame.

Accelerated operations

pandas has support for accelerating certain types of binary numerical and boolean operations using the numexpr library and the bottleneck libraries.

These libraries are especially useful when dealing with large data sets, and provide large speedups. numexpr uses smart chunking, caching, and multiple cores. bottleneck is a set of specialized cython routines that are especially fast when dealing with arrays that have nans.

Here is a sample (using 100 column x 100,000 row DataFrames):

Operation

0.11.0 (ms)

Prior Version (ms)

Ratio to Prior

df1 > df2

13.32

125.35

0.1063

df1 * df2

21.71

36.63

0.5928

df1 + df2

22.04

36.50

0.6039

You are highly encouraged to install both libraries. See the section Recommended Dependencies for more installation info.

These are both enabled to be used by default, you can control this by setting the options:

pd.set_option('compute.use_bottleneck', False)
pd.set_option('compute.use_numexpr', False)

Flexible binary operations

With binary operations between pandas data structures, there are two key points of interest:

  • Broadcasting behavior between higher- (e.g. DataFrame) and lower-dimensional (e.g. Series) objects.

  • Missing data in computations.

We will demonstrate how to manage these issues independently, though they can be handled simultaneously.

Matching / broadcasting behavior

DataFrame has the methods add(), sub(), mul(), div() and related functions radd(), rsub(), … for carrying out binary operations. For broadcasting behavior, Series input is of primary interest. Using these functions, you can use to either match on the index or columns via the axis keyword:

In [18]: df = pd.DataFrame({
   ....:     'one': pd.Series(np.random.randn(3), index=['a', 'b', 'c']),
   ....:     'two': pd.Series(np.random.randn(4), index=['a', 'b', 'c', 'd']),
   ....:     'three': pd.Series(np.random.randn(3), index=['b', 'c', 'd'])})
   ....: 

In [19]: df
Out[19]: 
        one       two     three
a  1.394981  1.772517       NaN
b  0.343054  1.912123 -0.050390
c  0.695246  1.478369  1.227435
d       NaN  0.279344 -0.613172

In [20]: row = df.iloc[1]

In [21]: column = df['two']

In [22]: df.sub(row, axis='columns')
Out[22]: 
        one       two     three
a  1.051928 -0.139606       NaN
b  0.000000  0.000000  0.000000
c  0.352192 -0.433754  1.277825
d       NaN -1.632779 -0.562782

In [23]: df.sub(row, axis=1)
Out[23]: 
        one       two     three
a  1.051928 -0.139606       NaN
b  0.000000  0.000000  0.000000
c  0.352192 -0.433754  1.277825
d       NaN -1.632779 -0.562782

In [24]: df.sub(column, axis='index')
Out[24]: 
        one  two     three
a -0.377535  0.0       NaN
b -1.569069  0.0 -1.962513
c -0.783123  0.0 -0.250933
d       NaN  0.0 -0.892516

In [25]: df.sub(column, axis=0)
Out[25]: 
        one  two     three
a -0.377535  0.0       NaN
b -1.569069  0.0 -1.962513
c -0.783123  0.0 -0.250933
d       NaN  0.0 -0.892516

Furthermore you can align a level of a MultiIndexed DataFrame with a Series.

In [26]: dfmi = df.copy()

In [27]: dfmi.index = pd.MultiIndex.from_tuples([(1, 'a'), (1, 'b'),
   ....:                                         (1, 'c'), (2, 'a')],
   ....:                                        names=['first', 'second'])
   ....: 

In [28]: dfmi.sub(column, axis=0, level='second')
Out[28]: 
                   one       two     three
first second                              
1     a      -0.377535  0.000000       NaN
      b      -1.569069  0.000000 -1.962513
      c      -0.783123  0.000000 -0.250933
2     a            NaN -1.493173 -2.385688

Series and Index also support the divmod() builtin. This function takes the floor division and modulo operation at the same time returning a two-tuple of the same type as the left hand side. For example:

In [29]: s = pd.Series(np.arange(10))

In [30]: s
Out[30]: 
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
dtype: int64

In [31]: div, rem = divmod(s, 3)

In [32]: div
Out[32]: 
0    0
1    0
2    0
3    1
4    1
5    1
6    2
7    2
8    2
9    3
dtype: int64

In [33]: rem
Out[33]: 
0    0
1    1
2    2
3    0
4    1
5    2
6    0
7    1
8    2
9    0
dtype: int64

In [34]: idx = pd.Index(np.arange(10))

In [35]: idx
Out[35]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')

In [36]: div, rem = divmod(idx, 3)

In [37]: div
Out[37]: Int64Index([0, 0, 0, 1, 1, 1, 2, 2, 2, 3], dtype='int64')

In [38]: rem
Out[38]: Int64Index([0, 1, 2, 0, 1, 2, 0, 1, 2, 0], dtype='int64')

We can also do elementwise divmod():

In [39]: div, rem = divmod(s, [2, 2, 3, 3, 4, 4, 5, 5, 6, 6])

In [40]: div
Out[40]: 
0    0
1    0
2    0
3    1
4    1
5    1
6    1
7    1
8    1
9    1
dtype: int64

In [41]: rem
Out[41]: 
0    0
1    1
2    2
3    0
4    0
5    1
6    1
7    2
8    2
9    3
dtype: int64

Missing data / operations with fill values

In Series and DataFrame, the arithmetic functions have the option of inputting a fill_value, namely a value to substitute when at most one of the values at a location are missing. For example, when adding two DataFrame objects, you may wish to treat NaN as 0 unless both DataFrames are missing that value, in which case the result will be NaN (you can later replace NaN with some other value using fillna if you wish).

In [42]: df
Out[42]: 
        one       two     three
a  1.394981  1.772517       NaN
b  0.343054  1.912123 -0.050390
c  0.695246  1.478369  1.227435
d       NaN  0.279344 -0.613172

In [43]: df2
Out[43]: 
        one       two     three
a  1.394981  1.772517  1.000000
b  0.343054  1.912123 -0.050390
c  0.695246  1.478369  1.227435
d       NaN  0.279344 -0.613172

In [44]: df + df2
Out[44]: 
        one       two     three
a  2.789963  3.545034       NaN
b  0.686107  3.824246 -0.100780
c  1.390491  2.956737  2.454870
d       NaN  0.558688 -1.226343

In [45]: df.add(df2, fill_value=0)
Out[45]: 
        one       two     three
a  2.789963  3.545034  1.000000
b  0.686107  3.824246 -0.100780
c  1.390491  2.956737  2.454870
d       NaN  0.558688 -1.226343

Flexible comparisons

Series and DataFrame have the binary comparison methods eq, ne, lt, gt, le, and ge whose behavior is analogous to the binary arithmetic operations described above:

In [46]: df.gt(df2)
Out[46]: 
     one    two  three
a  False  False  False
b  False  False  False
c  False  False  False
d  False  False  False

In [47]: df2.ne(df)
Out[47]: 
     one    two  three
a  False  False   True
b  False  False  False
c  False  False  False
d   True  False  False

These operations produce a pandas object of the same type as the left-hand-side input that is of dtype bool. These boolean objects can be used in indexing operations, see the section on Boolean indexing.

Boolean reductions

You can apply the reductions: empty, any(), all(), and bool() to provide a way to summarize a boolean result.

In [48]: (df > 0).all()
Out[48]: 
one      False
two       True
three    False
dtype: bool

In [49]: (df > 0).any()
Out[49]: 
one      True
two      True
three    True
dtype: bool

You can reduce to a final boolean value.

In [50]: (df > 0).any().any()
Out[50]: True

You can test if a pandas object is empty, via the empty property.

In [51]: df.empty
Out[51]: False

In [52]: pd.DataFrame(columns=list('ABC')).empty
Out[52]: True

To evaluate single-element pandas objects in a boolean context, use the method bool():

In [53]: pd.Series([True]).bool()
Out[53]: True

In [54]: pd.Series([False]).bool()
Out[54]: False

In [55]: pd.DataFrame([[True]]).bool()
Out[55]: True

In [56]: pd.DataFrame([[False]]).bool()
Out[56]: False

Warning

You might be tempted to do the following:

>>> if df:
...     pass

Or

>>> df and df2

These will both raise errors, as you are trying to compare multiple values.:

ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().

See gotchas for a more detailed discussion.

Comparing if objects are equivalent

Often you may find that there is more than one way to compute the same result. As a simple example, consider df + df and df * 2. To test that these two computations produce the same result, given the tools shown above, you might imagine using (df + df == df * 2).all(). But in fact, this expression is False:

In [57]: df + df == df * 2
Out[57]: 
     one   two  three
a   True  True  False
b   True  True   True
c   True  True   True
d  False  True   True

In [58]: (df + df == df * 2).all()
Out[58]: 
one      False
two       True
three    False
dtype: bool

Notice that the boolean DataFrame df + df == df * 2 contains some False values! This is because NaNs do not compare as equals:

In [59]: np.nan == np.nan
Out[59]: False

So, NDFrames (such as Series and DataFrames) have an equals() method for testing equality, with NaNs in corresponding locations treated as equal.

In [60]: (df + df).equals(df * 2)
Out[60]: True

Note that the Series or DataFrame index needs to be in the same order for equality to be True:

In [61]: df1 = pd.DataFrame({'col': ['foo', 0, np.nan]})

In [62]: df2 = pd.DataFrame({'col': [np.nan, 0, 'foo']}, index=[2, 1, 0])

In [63]: df1.equals(df2)
Out[63]: False

In [64]: df1.equals(df2.sort_index())
Out[64]: True

Comparing array-like objects

You can conveniently perform element-wise comparisons when comparing a pandas data structure with a scalar value:

In [65]: pd.Series(['foo', 'bar', 'baz']) == 'foo'
Out[65]: 
0     True
1    False
2    False
dtype: bool

In [66]: pd.Index(['foo', 'bar', 'baz']) == 'foo'
Out[66]: array([ True, False, False])

Pandas also handles element-wise comparisons between different array-like objects of the same length:

In [67]: pd.Series(['foo', 'bar', 'baz']) == pd.Index(['foo', 'bar', 'qux'])
Out[67]: 
0     True
1     True
2    False
dtype: bool

In [68]: pd.Series(['foo', 'bar', 'baz']) == np.array(['foo', 'bar', 'qux'])
Out[68]: 
0     True
1     True
2    False
dtype: bool

Trying to compare Index or Series objects of different lengths will raise a ValueError:

In [55]: pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo', 'bar'])
ValueError: Series lengths must match to compare

In [56]: pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo'])
ValueError: Series lengths must match to compare

Note that this is different from the NumPy behavior where a comparison can be broadcast:

In [69]: np.array([1, 2, 3]) == np.array([2])
Out[69]: array([False,  True, False])

or it can return False if broadcasting can not be done:

In [70]: np.array([1, 2, 3]) == np.array([1, 2])
Out[70]: False

Combining overlapping data sets

A problem occasionally arising is the combination of two similar data sets where values in one are preferred over the other. An example would be two data series representing a particular economic indicator where one is considered to be of “higher quality”. However, the lower quality series might extend further back in history or have more complete data coverage. As such, we would like to combine two DataFrame objects where missing values in one DataFrame are conditionally filled with like-labeled values from the other DataFrame. The function implementing this operation is combine_first(), which we illustrate:

In [71]: df1 = pd.DataFrame({'A': [1., np.nan, 3., 5., np.nan],
   ....:                     'B': [np.nan, 2., 3., np.nan, 6.]})
   ....: 

In [72]: df2 = pd.DataFrame({'A': [5., 2., 4., np.nan, 3., 7.],
   ....:                     'B': [np.nan, np.nan, 3., 4., 6., 8.]})
   ....: 

In [73]: df1
Out[73]: 
     A    B
0  1.0  NaN
1  NaN  2.0
2  3.0  3.0
3  5.0  NaN
4  NaN  6.0

In [74]: df2
Out[74]: 
     A    B
0  5.0  NaN
1  2.0  NaN
2  4.0  3.0
3  NaN  4.0
4  3.0  6.0
5  7.0  8.0

In [75]: df1.combine_first(df2)
Out[75]: 
     A    B
0  1.0  NaN
1  2.0  2.0
2  3.0  3.0
3  5.0  4.0
4  3.0  6.0
5  7.0  8.0

General DataFrame combine

The combine_first() method above calls the more general DataFrame.combine(). This method takes another DataFrame and a combiner function, aligns the input DataFrame and then passes the combiner function pairs of Series (i.e., columns whose names are the same).

So, for instance, to reproduce combine_first() as above:

In [76]: def combiner(x, y):
   ....:     return np.where(pd.isna(x), y, x)
   ....: 

Descriptive statistics

There exists a large number of methods for computing descriptive statistics and other related operations on Series, DataFrame. Most of these are aggregations (hence producing a lower-dimensional result) like sum(), mean(), and quantile(), but some of them, like cumsum() and cumprod(), produce an object of the same size. Generally speaking, these methods take an axis argument, just like ndarray.{sum, std, …}, but the axis can be specified by name or integer:

  • Series: no axis argument needed

  • DataFrame: “index” (axis=0, default), “columns” (axis=1)

For example:

In [77]: df
Out[77]: 
        one       two     three
a  1.394981  1.772517       NaN
b  0.343054  1.912123 -0.050390
c  0.695246  1.478369  1.227435
d       NaN  0.279344 -0.613172

In [78]: df.mean(0)
Out[78]: 
one      0.811094
two      1.360588
three    0.187958
dtype: float64

In [79]: df.mean(1)
Out[79]: 
a    1.583749
b    0.734929
c    1.133683
d   -0.166914
dtype: float64

All such methods have a skipna option signaling whether to exclude missing data (True by default):

In [80]: df.sum(0, skipna=False)
Out[80]: 
one           NaN
two      5.442353
three         NaN
dtype: float64

In [81]: df.sum(axis=1, skipna=True)
Out[81]: 
a    3.167498
b    2.204786
c    3.401050
d   -0.333828
dtype: float64

Combined with the broadcasting / arithmetic behavior, one can describe various statistical procedures, like standardization (rendering data zero mean and standard deviation 1), very concisely:

In [82]: ts_stand = (df - df.mean()) / df.std()

In [83]: ts_stand.std()
Out[83]: 
one      1.0
two      1.0
three    1.0
dtype: float64

In [84]: xs_stand = df.sub(df.mean(1), axis=0).div(df.std(1), axis=0)

In [85]: xs_stand.std(1)
Out[85]: 
a    1.0
b    1.0
c    1.0
d    1.0
dtype: float64

Note that methods like cumsum() and cumprod() preserve the location of NaN values. This is somewhat different from expanding() and rolling(). For more details please see this note.

In [86]: df.cumsum()
Out[86]: 
        one       two     three
a  1.394981  1.772517       NaN
b  1.738035  3.684640 -0.050390
c  2.433281  5.163008  1.177045
d       NaN  5.442353  0.563873

Here is a quick reference summary table of common functions. Each also takes an optional level parameter which applies only if the object has a hierarchical index.

Function

Description

count

Number of non-NA observations

sum

Sum of values

mean

Mean of values

mad

Mean absolute deviation

median

Arithmetic median of values

min

Minimum

max

Maximum

mode

Mode

abs

Absolute Value

prod

Product of values

std

Bessel-corrected sample standard deviation

var

Unbiased variance

sem

Standard error of the mean

skew

Sample skewness (3rd moment)

kurt

Sample kurtosis (4th moment)

quantile

Sample quantile (value at %)

cumsum

Cumulative sum

cumprod

Cumulative product

cummax

Cumulative maximum

cummin

Cumulative minimum

Note that by chance some NumPy methods, like mean, std, and sum, will exclude NAs on Series input by default:

In [87]: np.mean(df['one'])
Out[87]: 0.8110935116651192

In [88]: np.mean(df['one'].to_numpy())
Out[88]: nan

Series.nunique() will return the number of unique non-NA values in a Series:

In [89]: series = pd.Series(np.random.randn(500))

In [90]: series[20:500] = np.nan

In [91]: series[10:20] = 5

In [92]: series.nunique()
Out[92]: 11

Summarizing data: describe

There is a convenient describe() function which computes a variety of summary statistics about a Series or the columns of a DataFrame (excluding NAs of course):

In [93]: series = pd.Series(np.random.randn(1000))

In [94]: series[::2] = np.nan

In [95]: series.describe()
Out[95]: 
count    500.000000
mean      -0.021292
std        1.015906
min       -2.683763
25%       -0.699070
50%       -0.069718
75%        0.714483
max        3.160915
dtype: float64

In [96]: frame = pd.DataFrame(np.random.randn(1000, 5),
   ....:                      columns=['a', 'b', 'c', 'd', 'e'])
   ....: 

In [97]: frame.iloc[::2] = np.nan

In [98]: frame.describe()
Out[98]: 
                a           b           c           d           e
count  500.000000  500.000000  500.000000  500.000000  500.000000
mean     0.033387    0.030045   -0.043719   -0.051686    0.005979
std      1.017152    0.978743    1.025270    1.015988    1.006695
min     -3.000951   -2.637901   -3.303099   -3.159200   -3.188821
25%     -0.647623   -0.576449   -0.712369   -0.691338   -0.691115
50%      0.047578   -0.021499   -0.023888   -0.032652   -0.025363
75%      0.729907    0.775880    0.618896    0.670047    0.649748
max      2.740139    2.752332    3.004229    2.728702    3.240991

You can select specific percentiles to include in the output:

In [99]: series.describe(percentiles=[.05, .25, .75, .95])
Out[99]: 
count    500.000000
mean      -0.021292
std        1.015906
min       -2.683763
5%        -1.645423
25%       -0.699070
50%       -0.069718
75%        0.714483
95%        1.711409
max        3.160915
dtype: float64

By default, the median is always included.

For a non-numerical Series object, describe() will give a simple summary of the number of unique values and most frequently occurring values:

In [100]: s = pd.Series(['a', 'a', 'b', 'b', 'a', 'a', np.nan, 'c', 'd', 'a'])

In [101]: s.describe()
Out[101]: 
count     9
unique    4
top       a
freq      5
dtype: object

Note that on a mixed-type DataFrame object, describe() will restrict the summary to include only numerical columns or, if none are, only categorical columns:

In [102]: frame = pd.DataFrame({'a': ['Yes', 'Yes', 'No', 'No'], 'b': range(4)})

In [103]: frame.describe()
Out[103]: 
              b
count  4.000000
mean   1.500000
std    1.290994
min    0.000000
25%    0.750000
50%    1.500000
75%    2.250000
max    3.000000

This behavior can be controlled by providing a list of types as include/exclude arguments. The special value all can also be used:

In [104]: frame.describe(include=['object'])
Out[104]: 
         a
count    4
unique   2
top     No
freq     2

In [105]: frame.describe(include=['number'])
Out[105]: 
              b
count  4.000000
mean   1.500000
std    1.290994
min    0.000000
25%    0.750000
50%    1.500000
75%    2.250000
max    3.000000

In [106]: frame.describe(include='all')
Out[106]: 
          a         b
count     4  4.000000
unique    2       NaN
top      No       NaN
freq      2       NaN
mean    NaN  1.500000
std     NaN  1.290994
min     NaN  0.000000
25%     NaN  0.750000
50%     NaN  1.500000
75%     NaN  2.250000
max     NaN  3.000000

That feature relies on select_dtypes. Refer to there for details about accepted inputs.

Index of min/max values

The idxmin() and idxmax() functions on Series and DataFrame compute the index labels with the minimum and maximum corresponding values:

In [107]: s1 = pd.Series(np.random.randn(5))

In [108]: s1
Out[108]: 
0    1.118076
1   -0.352051
2   -1.242883
3   -1.277155
4   -0.641184
dtype: float64

In [109]: s1.idxmin(), s1.idxmax()
Out[109]: (3, 0)

In [110]: df1 = pd.DataFrame(np.random.randn(5, 3), columns=['A', 'B', 'C'])

In [111]: df1
Out[111]: 
          A         B         C
0 -0.327863 -0.946180 -0.137570
1 -0.186235 -0.257213 -0.486567
2 -0.507027 -0.871259 -0.111110
3  2.000339 -2.430505  0.089759
4 -0.321434 -0.033695  0.096271

In [112]: df1.idxmin(axis=0)
Out[112]: 
A    2
B    3
C    1
dtype: int64

In [113]: df1.idxmax(axis=1)
Out[113]: 
0    C
1    A
2    C
3    A
4    C
dtype: object

When there are multiple rows (or columns) matching the minimum or maximum value, idxmin() and idxmax() return the first matching index:

In [114]: df3 = pd.DataFrame([2, 1, 1, 3, np.nan], columns=['A'], index=list('edcba'))

In [115]: df3
Out[115]: 
     A
e  2.0
d  1.0
c  1.0
b  3.0
a  NaN

In [116]: df3['A'].idxmin()
Out[116]: 'd'

Note

idxmin and idxmax are called argmin and argmax in NumPy.

Value counts (histogramming) / mode

The value_counts() Series method and top-level function computes a histogram of a 1D array of values. It can also be used as a function on regular arrays:

In [117]: data = np.random.randint(0, 7, size=50)

In [118]: data
Out[118]: 
array([6, 6, 2, 3, 5, 3, 2, 5, 4, 5, 4, 3, 4, 5, 0, 2, 0, 4, 2, 0, 3, 2,
       2, 5, 6, 5, 3, 4, 6, 4, 3, 5, 6, 4, 3, 6, 2, 6, 6, 2, 3, 4, 2, 1,
       6, 2, 6, 1, 5, 4])

In [119]: s = pd.Series(data)

In [120]: s.value_counts()
Out[120]: 
6    10
2    10
4     9
5     8
3     8
0     3
1     2
dtype: int64

In [121]: pd.value_counts(data)
Out[121]: 
6    10
2    10
4     9
5     8
3     8
0     3
1     2
dtype: int64

Similarly, you can get the most frequently occurring value(s) (the mode) of the values in a Series or DataFrame:

In [122]: s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])

In [123]: s5.mode()
Out[123]: 
0    3
1    7
dtype: int64

In [124]: df5 = pd.DataFrame({"A": np.random.randint(0, 7, size=50),
   .....:                     "B": np.random.randint(-10, 15, size=50)})
   .....: 

In [125]: df5.mode()
Out[125]: 
     A   B
0  1.0  -9
1  NaN  10
2  NaN  13

Discretization and quantiling

Continuous values can be discretized using the cut() (bins based on values) and qcut() (bins based on sample quantiles) functions:

In [126]: arr = np.random.randn(20)

In [127]: factor = pd.cut(arr, 4)

In [128]: factor
Out[128]: 
[(-0.251, 0.464], (-0.968, -0.251], (0.464, 1.179], (-0.251, 0.464], (-0.968, -0.251], ..., (-0.251, 0.464], (-0.968, -0.251], (-0.968, -0.251], (-0.968, -0.251], (-0.968, -0.251]]
Length: 20
Categories (4, interval[float64]): [(-0.968, -0.251] < (-0.251, 0.464] < (0.464, 1.179] <
                                    (1.179, 1.893]]

In [129]: factor = pd.cut(arr, [-5, -1, 0, 1, 5])

In [130]: factor
Out[130]: 
[(0, 1], (-1, 0], (0, 1], (0, 1], (-1, 0], ..., (-1, 0], (-1, 0], (-1, 0], (-1, 0], (-1, 0]]
Length: 20
Categories (4, interval[int64]): [(-5, -1] < (-1, 0] < (0, 1] < (1, 5]]

qcut() computes sample quantiles. For example, we could slice up some normally distributed data into equal-size quartiles like so:

In [131]: arr = np.random.randn(30)

In [132]: factor = pd.qcut(arr, [0, .25, .5, .75, 1])

In [133]: factor
Out[133]: 
[(0.569, 1.184], (-2.278, -0.301], (-2.278, -0.301], (0.569, 1.184], (0.569, 1.184], ..., (-0.301, 0.569], (1.184, 2.346], (1.184, 2.346], (-0.301, 0.569], (-2.278, -0.301]]
Length: 30
Categories (4, interval[float64]): [(-2.278, -0.301] < (-0.301, 0.569] < (0.569, 1.184] <
                                    (1.184, 2.346]]

In [134]: pd.value_counts(factor)
Out[134]: 
(1.184, 2.346]      8
(-2.278, -0.301]    8
(0.569, 1.184]      7
(-0.301, 0.569]     7
dtype: int64

We can also pass infinite values to define the bins:

In [135]: arr = np.random.randn(20)

In [136]: factor = pd.cut(arr, [-np.inf, 0, np.inf])

In [137]: factor
Out[137]: 
[(-inf, 0.0], (0.0, inf], (0.0, inf], (-inf, 0.0], (-inf, 0.0], ..., (-inf, 0.0], (-inf, 0.0], (-inf, 0.0], (0.0, inf], (0.0, inf]]
Length: 20
Categories (2, interval[float64]): [(-inf, 0.0] < (0.0, inf]]

Function application

To apply your own or another library’s functions to pandas objects, you should be aware of the three methods below. The appropriate method to use depends on whether your function expects to operate on an entire DataFrame or Series, row- or column-wise, or elementwise.

  1. Tablewise Function Application: pipe()

  2. Row or Column-wise Function Application: apply()

  3. Aggregation API: agg() and transform()

  4. Applying Elementwise Functions: applymap()

Tablewise function application

DataFrames and Series can be passed into functions. However, if the function needs to be called in a chain, consider using the pipe() method.

First some setup:

In [138]: def extract_city_name(df):
   .....:     """
   .....:     Chicago, IL -> Chicago for city_name column
   .....:     """
   .....:     df['city_name'] = df['city_and_code'].str.split(",").str.get(0)
   .....:     return df
   .....: 

In [139]: def add_country_name(df, country_name=None):
   .....:     """
   .....:     Chicago -> Chicago-US for city_name column
   .....:     """
   .....:     col = 'city_name'
   .....:     df['city_and_country'] = df[col] + country_name
   .....:     return df
   .....: 

In [140]: df_p = pd.DataFrame({'city_and_code': ['Chicago, IL']})

extract_city_name and add_country_name are functions taking and returning DataFrames.

Now compare the following:

In [141]: add_country_name(extract_city_name(df_p), country_name='US')
Out[141]: 
  city_and_code city_name city_and_country
0   Chicago, IL   Chicago        ChicagoUS

Is equivalent to:

In [142]: (df_p.pipe(extract_city_name)
   .....:      .pipe(add_country_name, country_name="US"))
   .....: 
Out[142]: 
  city_and_code city_name city_and_country
0   Chicago, IL   Chicago        ChicagoUS

Pandas encourages the second style, which is known as method chaining. pipe makes it easy to use your own or another library’s functions in method chains, alongside pandas’ methods.

In the example above, the functions extract_city_name and add_country_name each expected a DataFrame as the first positional argument. What if the function you wish to apply takes its data as, say, the second argument? In this case, provide pipe with a tuple of (callable, data_keyword). .pipe will route the DataFrame to the argument specified in the tuple.

For example, we can fit a regression using statsmodels. Their API expects a formula first and a DataFrame as the second argument, data. We pass in the function, keyword pair (sm.ols, 'data') to pipe:

In [143]: import statsmodels.formula.api as sm

In [144]: bb = pd.read_csv('data/baseball.csv', index_col='id')

In [145]: (bb.query('h > 0')
   .....:    .assign(ln_h=lambda df: np.log(df.h))
   .....:    .pipe((sm.ols, 'data'), 'hr ~ ln_h + year + g + C(lg)')
   .....:    .fit()
   .....:    .summary()
   .....:  )
   .....: 
Out[145]: 
<class 'statsmodels.iolib.summary.Summary'>
"""
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                     hr   R-squared:                       0.685
Model:                            OLS   Adj. R-squared:                  0.665
Method:                 Least Squares   F-statistic:                     34.28
Date:                Wed, 05 Feb 2020   Prob (F-statistic):           3.48e-15
Time:                        17:36:55   Log-Likelihood:                -205.92
No. Observations:                  68   AIC:                             421.8
Df Residuals:                      63   BIC:                             432.9
Df Model:                           4                                         
Covariance Type:            nonrobust                                         
===============================================================================
                  coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------
Intercept   -8484.7720   4664.146     -1.819      0.074   -1.78e+04     835.780
C(lg)[T.NL]    -2.2736      1.325     -1.716      0.091      -4.922       0.375
ln_h           -1.3542      0.875     -1.547      0.127      -3.103       0.395
year            4.2277      2.324      1.819      0.074      -0.417       8.872
g               0.1841      0.029      6.258      0.000       0.125       0.243
==============================================================================
Omnibus:                       10.875   Durbin-Watson:                   1.999
Prob(Omnibus):                  0.004   Jarque-Bera (JB):               17.298
Skew:                           0.537   Prob(JB):                     0.000175
Kurtosis:                       5.225   Cond. No.                     1.49e+07
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.49e+07. This might indicate that there are
strong multicollinearity or other numerical problems.
"""

The pipe method is inspired by unix pipes and more recently dplyr and magrittr, which have introduced the popular (%>%) (read pipe) operator for R. The implementation of pipe here is quite clean and feels right at home in python. We encourage you to view the source code of pipe().

Row or column-wise function application

Arbitrary functions can be applied along the axes of a DataFrame using the apply() method, which, like the descriptive statistics methods, takes an optional axis argument:

In [146]: df.apply(np.mean)
Out[146]: 
one      0.811094
two      1.360588
three    0.187958
dtype: float64

In [147]: df.apply(np.mean, axis=1)
Out[147]: 
a    1.583749
b    0.734929
c    1.133683
d   -0.166914
dtype: float64

In [148]: df.apply(lambda x: x.max() - x.min())
Out[148]: 
one      1.051928
two      1.632779
three    1.840607
dtype: float64

In [149]: df.apply(np.cumsum)
Out[149]: 
        one       two     three
a  1.394981  1.772517       NaN
b  1.738035  3.684640 -0.050390
c  2.433281  5.163008  1.177045
d       NaN  5.442353  0.563873

In [150]: df.apply(np.exp)
Out[150]: 
        one       two     three
a  4.034899  5.885648       NaN
b  1.409244  6.767440  0.950858
c  2.004201  4.385785  3.412466
d       NaN  1.322262  0.541630

The apply() method will also dispatch on a string method name.

In [151]: df.apply('mean')
Out[151]: 
one      0.811094
two      1.360588
three    0.187958
dtype: float64

In [152]: df.apply('mean', axis=1)
Out[152]: 
a    1.583749
b    0.734929
c    1.133683
d   -0.166914
dtype: float64

The return type of the function passed to apply() affects the type of the final output from DataFrame.apply for the default behaviour:

  • If the applied function returns a Series, the final output is a DataFrame. The columns match the index of the Series returned by the applied function.

  • If the applied function returns any other type, the final output is a Series.

This default behaviour can be overridden using the result_type, which accepts three options: reduce, broadcast, and expand. These will determine how list-likes return values expand (or not) to a DataFrame.

apply() combined with some cleverness can be used to answer many questions about a data set. For example, suppose we wanted to extract the date where the maximum value for each column occurred:

In [153]: tsdf = pd.DataFrame(np.random.randn(1000, 3), columns=['A', 'B', 'C'],
   .....:                     index=pd.date_range('1/1/2000', periods=1000))
   .....: 

In [154]: tsdf.apply(lambda x: x.idxmax())
Out[154]: 
A   2000-08-06
B   2001-01-18
C   2001-07-18
dtype: datetime64[ns]

You may also pass additional arguments and keyword arguments to the apply() method. For instance, consider the following function you would like to apply:

def subtract_and_divide(x, sub, divide=1):
    return (x - sub) / divide

You may then apply this function as follows:

df.apply(subtract_and_divide, args=(5,), divide=3)

Another useful feature is the ability to pass Series methods to carry out some Series operation on each column or row:

In [155]: tsdf
Out[155]: 
                   A         B         C
2000-01-01 -0.158131 -0.232466  0.321604
2000-01-02 -1.810340 -3.105758  0.433834
2000-01-03 -1.209847 -1.156793 -0.136794
2000-01-04       NaN       NaN       NaN
2000-01-05       NaN       NaN       NaN
2000-01-06       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN
2000-01-08 -0.653602  0.178875  1.008298
2000-01-09  1.007996  0.462824  0.254472
2000-01-10  0.307473  0.600337  1.643950

In [156]: tsdf.apply(pd.Series.interpolate)
Out[156]: 
                   A         B         C
2000-01-01 -0.158131 -0.232466  0.321604
2000-01-02 -1.810340 -3.105758  0.433834
2000-01-03 -1.209847 -1.156793 -0.136794
2000-01-04 -1.098598 -0.889659  0.092225
2000-01-05 -0.987349 -0.622526  0.321243
2000-01-06 -0.876100 -0.355392  0.550262
2000-01-07 -0.764851 -0.088259  0.779280
2000-01-08 -0.653602  0.178875  1.008298
2000-01-09  1.007996  0.462824  0.254472
2000-01-10  0.307473  0.600337  1.643950

Finally, apply() takes an argument raw which is False by default, which converts each row or column into a Series before applying the function. When set to True, the passed function will instead receive an ndarray object, which has positive performance implications if you do not need the indexing functionality.

Aggregation API

The aggregation API allows one to express possibly multiple aggregation operations in a single concise way. This API is similar across pandas objects, see groupby API, the window functions API, and the resample API. The entry point for aggregation is DataFrame.aggregate(), or the alias DataFrame.agg().

We will use a similar starting frame from above:

In [157]: tsdf = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
   .....:                     index=pd.date_range('1/1/2000', periods=10))
   .....: 

In [158]: tsdf.iloc[3:7] = np.nan

In [159]: tsdf
Out[159]: 
                   A         B         C
2000-01-01  1.257606  1.004194  0.167574
2000-01-02 -0.749892  0.288112 -0.757304
2000-01-03 -0.207550 -0.298599  0.116018
2000-01-04       NaN       NaN       NaN
2000-01-05       NaN       NaN       NaN
2000-01-06       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN
2000-01-08  0.814347 -0.257623  0.869226
2000-01-09 -0.250663 -1.206601  0.896839
2000-01-10  2.169758 -1.333363  0.283157

Using a single function is equivalent to apply(). You can also pass named methods as strings. These will return a Series of the aggregated output:

In [160]: tsdf.agg(np.sum)
Out[160]: 
A    3.033606
B   -1.803879
C    1.575510
dtype: float64

In [161]: tsdf.agg('sum')
Out[161]: 
A    3.033606
B   -1.803879
C    1.575510
dtype: float64

# these are equivalent to a ``.sum()`` because we are aggregating
# on a single function
In [162]: tsdf.sum()
Out[162]: 
A    3.033606
B   -1.803879
C    1.575510
dtype: float64

Single aggregations on a Series this will return a scalar value:

In [163]: tsdf['A'].agg('sum')
Out[163]: 3.033606102414146

Aggregating with multiple functions

You can pass multiple aggregation arguments as a list. The results of each of the passed functions will be a row in the resulting DataFrame. These are naturally named from the aggregation function.

In [164]: tsdf.agg(['sum'])
Out[164]: 
            A         B        C
sum  3.033606 -1.803879  1.57551

Multiple functions yield multiple rows:

In [165]: tsdf.agg(['sum', 'mean'])
Out[165]: 
             A         B         C
sum   3.033606 -1.803879  1.575510
mean  0.505601 -0.300647  0.262585

On a Series, multiple functions return a Series, indexed by the function names:

In [166]: tsdf['A'].agg(['sum', 'mean'])
Out[166]: 
sum     3.033606
mean    0.505601
Name: A, dtype: float64

Passing a lambda function will yield a <lambda> named row:

In [167]: tsdf['A'].agg(['sum', lambda x: x.mean()])
Out[167]: 
sum         3.033606
<lambda>    0.505601
Name: A, dtype: float64

Passing a named function will yield that name for the row:

In [168]: def mymean(x):
   .....:     return x.mean()
   .....: 

In [169]: tsdf['A'].agg(['sum', mymean])
Out[169]: 
sum       3.033606
mymean    0.505601
Name: A, dtype: float64

Aggregating with a dict

Passing a dictionary of column names to a scalar or a list of scalars, to DataFrame.agg allows you to customize which functions are applied to which columns. Note that the results are not in any particular order, you can use an OrderedDict instead to guarantee ordering.

In [170]: tsdf.agg({'A': 'mean', 'B': 'sum'})
Out[170]: 
A    0.505601
B   -1.803879
dtype: float64

Passing a list-like will generate a DataFrame output. You will get a matrix-like output of all of the aggregators. The output will consist of all unique functions. Those that are not noted for a particular column will be NaN:

In [171]: tsdf.agg({'A': ['mean', 'min'], 'B': 'sum'})
Out[171]: 
             A         B
mean  0.505601       NaN
min  -0.749892       NaN
sum        NaN -1.803879

Mixed dtypes

When presented with mixed dtypes that cannot aggregate, .agg will only take the valid aggregations. This is similar to how groupby .agg works.

In [172]: mdf = pd.DataFrame({'A': [1, 2, 3],
   .....:                     'B': [1., 2., 3.],
   .....:                     'C': ['foo', 'bar', 'baz'],
   .....:                     'D': pd.date_range('20130101', periods=3)})
   .....: 

In [173]: mdf.dtypes
Out[173]: 
A             int64
B           float64
C            object
D    datetime64[ns]
dtype: object
In [174]: mdf.agg(['min', 'sum'])
Out[174]: 
     A    B          C          D
min  1  1.0        bar 2013-01-01
sum  6  6.0  foobarbaz        NaT

Custom describe

With .agg() is it possible to easily create a custom describe function, similar to the built in describe function.

In [175]: from functools import partial

In [176]: q_25 = partial(pd.Series.quantile, q=0.25)

In [177]: q_25.__name__ = '25%'

In [178]: q_75 = partial(pd.Series.quantile, q=0.75)

In [179]: q_75.__name__ = '75%'

In [180]: tsdf.agg(['count', 'mean', 'std', 'min', q_25, 'median', q_75, 'max'])
Out[180]: 
               A         B         C
count   6.000000  6.000000  6.000000
mean    0.505601 -0.300647  0.262585
std     1.103362  0.887508  0.606860
min    -0.749892 -1.333363 -0.757304
25%    -0.239885 -0.979600  0.128907
median  0.303398 -0.278111  0.225365
75%     1.146791  0.151678  0.722709
max     2.169758  1.004194  0.896839

Transform API

The transform() method returns an object that is indexed the same (same size) as the original. This API allows you to provide multiple operations at the same time rather than one-by-one. Its API is quite similar to the .agg API.

We create a frame similar to the one used in the above sections.

In [181]: tsdf = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
   .....:                     index=pd.date_range('1/1/2000', periods=10))
   .....: 

In [182]: tsdf.iloc[3:7] = np.nan

In [183]: tsdf
Out[183]: 
                   A         B         C
2000-01-01 -0.428759 -0.864890 -0.675341
2000-01-02 -0.168731  1.338144 -1.279321
2000-01-03 -1.621034  0.438107  0.903794
2000-01-04       NaN       NaN       NaN
2000-01-05       NaN       NaN       NaN
2000-01-06       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN
2000-01-08  0.254374 -1.240447 -0.201052
2000-01-09 -0.157795  0.791197 -1.144209
2000-01-10 -0.030876  0.371900  0.061932

Transform the entire frame. .transform() allows input functions as: a NumPy function, a string function name or a user defined function.

In [184]: tsdf.transform(np.abs)
Out[184]: 
                   A         B         C
2000-01-01  0.428759  0.864890  0.675341
2000-01-02  0.168731  1.338144  1.279321
2000-01-03  1.621034  0.438107  0.903794
2000-01-04       NaN       NaN       NaN
2000-01-05       NaN       NaN       NaN
2000-01-06       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN
2000-01-08  0.254374  1.240447  0.201052
2000-01-09  0.157795  0.791197  1.144209
2000-01-10  0.030876  0.371900  0.061932

In [185]: tsdf.transform('abs')
Out[185]: 
                   A         B         C
2000-01-01  0.428759  0.864890  0.675341
2000-01-02  0.168731  1.338144  1.279321
2000-01-03  1.621034  0.438107  0.903794
2000-01-04       NaN       NaN       NaN
2000-01-05       NaN       NaN       NaN
2000-01-06       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN
2000-01-08  0.254374  1.240447  0.201052
2000-01-09  0.157795  0.791197  1.144209
2000-01-10  0.030876  0.371900  0.061932

In [186]: tsdf.transform(lambda x: x.abs())
Out[186]: 
                   A         B         C
2000-01-01  0.428759  0.864890  0.675341
2000-01-02  0.168731  1.338144  1.279321
2000-01-03  1.621034  0.438107  0.903794
2000-01-04       NaN       NaN       NaN
2000-01-05       NaN       NaN       NaN
2000-01-06       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN
2000-01-08  0.254374  1.240447  0.201052
2000-01-09  0.157795  0.791197  1.144209
2000-01-10  0.030876  0.371900  0.061932

Here transform() received a single function; this is equivalent to a ufunc application.

In [187]: np.abs(tsdf)
Out[187]: 
                   A         B         C
2000-01-01  0.428759  0.864890  0.675341
2000-01-02  0.168731  1.338144  1.279321
2000-01-03  1.621034  0.438107  0.903794
2000-01-04       NaN       NaN       NaN
2000-01-05       NaN       NaN       NaN
2000-01-06       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN
2000-01-08  0.254374  1.240447  0.201052
2000-01-09  0.157795  0.791197  1.144209
2000-01-10  0.030876  0.371900  0.061932

Passing a single function to .transform() with a Series will yield a single Series in return.

In [188]: tsdf['A'].transform(np.abs)
Out[188]: 
2000-01-01    0.428759
2000-01-02    0.168731
2000-01-03    1.621034
2000-01-04         NaN
2000-01-05         NaN
2000-01-06         NaN
2000-01-07         NaN
2000-01-08    0.254374
2000-01-09    0.157795
2000-01-10    0.030876
Freq: D, Name: A, dtype: float64

Transform with multiple functions

Passing multiple functions will yield a column MultiIndexed DataFrame. The first level will be the original frame column names; the second level will be the names of the transforming functions.

In [189]: tsdf.transform([np.abs, lambda x: x + 1])
Out[189]: 
                   A                   B                   C          
            absolute  <lambda>  absolute  <lambda>  absolute  <lambda>
2000-01-01  0.428759  0.571241  0.864890  0.135110  0.675341  0.324659
2000-01-02  0.168731  0.831269  1.338144  2.338144  1.279321 -0.279321
2000-01-03  1.621034 -0.621034  0.438107  1.438107  0.903794  1.903794
2000-01-04       NaN       NaN       NaN       NaN       NaN       NaN
2000-01-05       NaN       NaN       NaN       NaN       NaN       NaN
2000-01-06       NaN       NaN       NaN       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN       NaN       NaN       NaN
2000-01-08  0.254374  1.254374  1.240447 -0.240447  0.201052  0.798948
2000-01-09  0.157795  0.842205  0.791197  1.791197  1.144209 -0.144209
2000-01-10  0.030876  0.969124  0.371900  1.371900  0.061932  1.061932

Passing multiple functions to a Series will yield a DataFrame. The resulting column names will be the transforming functions.

In [190]: tsdf['A'].transform([np.abs, lambda x: x + 1])
Out[190]: 
            absolute  <lambda>
2000-01-01  0.428759  0.571241
2000-01-02  0.168731  0.831269
2000-01-03  1.621034 -0.621034
2000-01-04       NaN       NaN
2000-01-05       NaN       NaN
2000-01-06       NaN       NaN
2000-01-07       NaN       NaN
2000-01-08  0.254374  1.254374
2000-01-09  0.157795  0.842205
2000-01-10  0.030876  0.969124

Transforming with a dict

Passing a dict of functions will allow selective transforming per column.

In [191]: tsdf.transform({'A': np.abs, 'B': lambda x: x + 1})
Out[191]: 
                   A         B
2000-01-01  0.428759  0.135110
2000-01-02  0.168731  2.338144
2000-01-03  1.621034  1.438107
2000-01-04       NaN       NaN
2000-01-05       NaN       NaN
2000-01-06       NaN       NaN
2000-01-07       NaN       NaN
2000-01-08  0.254374 -0.240447
2000-01-09  0.157795  1.791197
2000-01-10  0.030876  1.371900

Passing a dict of lists will generate a MultiIndexed DataFrame with these selective transforms.

In [192]: tsdf.transform({'A': np.abs, 'B': [lambda x: x + 1, 'sqrt']})
Out[192]: 
                   A         B          
            absolute  <lambda>      sqrt
2000-01-01  0.428759  0.135110       NaN
2000-01-02  0.168731  2.338144  1.156782
2000-01-03  1.621034  1.438107  0.661897
2000-01-04       NaN       NaN       NaN
2000-01-05       NaN       NaN       NaN
2000-01-06       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN
2000-01-08  0.254374 -0.240447       NaN
2000-01-09  0.157795  1.791197  0.889493
2000-01-10  0.030876  1.371900  0.609836

Applying elementwise functions

Since not all functions can be vectorized (accept NumPy arrays and return another array or value), the methods applymap() on DataFrame and analogously map() on Series accept any Python function taking a single value and returning a single value. For example:

In [193]: df4
Out[193]: 
        one       two     three
a  1.394981  1.772517       NaN
b  0.343054  1.912123 -0.050390
c  0.695246  1.478369  1.227435
d       NaN  0.279344 -0.613172

In [194]: def f(x):
   .....:     return len(str(x))
   .....: 

In [195]: df4['one'].map(f)
Out[195]: 
a    18
b    19
c    18
d     3
Name: one, dtype: int64

In [196]: df4.applymap(f)
Out[196]: 
   one  two  three
a   18   17      3
b   19   18     20
c   18   18     16
d    3   19     19

Series.map() has an additional feature; it can be used to easily “link” or “map” values defined by a secondary series. This is closely related to merging/joining functionality:

In [197]: s = pd.Series(['six', 'seven', 'six', 'seven', 'six'],
   .....:               index=['a', 'b', 'c', 'd', 'e'])
   .....: 

In [198]: t = pd.Series({'six': 6., 'seven': 7.})

In [199]: s
Out[199]: 
a      six
b    seven
c      six
d    seven
e      six
dtype: object

In [200]: s.map(t)
Out[200]: 
a    6.0
b    7.0
c    6.0
d    7.0
e    6.0
dtype: float64

Reindexing and altering labels

reindex() is the fundamental data alignment method in pandas. It is used to implement nearly all other features relying on label-alignment functionality. To reindex means to conform the data to match a given set of labels along a particular axis. This accomplishes several things:

  • Reorders the existing data to match a new set of labels

  • Inserts missing value (NA) markers in label locations where no data for that label existed

  • If specified, fill data for missing labels using logic (highly relevant to working with time series data)

Here is a simple example:

In [201]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

In [202]: s
Out[202]: 
a    1.695148
b    1.328614
c    1.234686
d   -0.385845
e   -1.326508
dtype: float64

In [203]: s.reindex(['e', 'b', 'f', 'd'])
Out[203]: 
e   -1.326508
b    1.328614
f         NaN
d   -0.385845
dtype: float64

Here, the f label was not contained in the Series and hence appears as NaN in the result.

With a DataFrame, you can simultaneously reindex the index and columns:

In [204]: df
Out[204]: 
        one       two     three
a  1.394981  1.772517       NaN
b  0.343054  1.912123 -0.050390
c  0.695246  1.478369  1.227435
d       NaN  0.279344 -0.613172

In [205]: df.reindex(index=['c', 'f', 'b'], columns=['three', 'two', 'one'])
Out[205]: 
      three       two       one
c  1.227435  1.478369  0.695246
f       NaN       NaN       NaN
b -0.050390  1.912123  0.343054

You may also use reindex with an axis keyword:

In [206]: df.reindex(['c', 'f', 'b'], axis='index')
Out[206]: 
        one       two     three
c  0.695246  1.478369  1.227435
f       NaN       NaN       NaN
b  0.343054  1.912123 -0.050390

Note that the Index objects containing the actual axis labels can be shared between objects. So if we have a Series and a DataFrame, the following can be done:

In [207]: rs = s.reindex(df.index)

In [208]: rs
Out[208]: 
a    1.695148
b    1.328614
c    1.234686
d   -0.385845
dtype: float64

In [209]: rs.index is df.index
Out[209]: True

This means that the reindexed Series’s index is the same Python object as the DataFrame’s index.

New in version 0.21.0.

DataFrame.reindex() also supports an “axis-style” calling convention, where you specify a single labels argument and the axis it applies to.

In [210]: df.reindex(['c', 'f', 'b'], axis='index')
Out[210]: 
        one       two     three
c  0.695246  1.478369  1.227435
f       NaN       NaN       NaN
b  0.343054  1.912123 -0.050390

In [211]: df.reindex(['three', 'two', 'one'], axis='columns')
Out[211]: 
      three       two       one
a       NaN  1.772517  1.394981
b -0.050390  1.912123  0.343054
c  1.227435  1.478369  0.695246
d -0.613172  0.279344       NaN

See also

MultiIndex / Advanced Indexing is an even more concise way of doing reindexing.

Note

When writing performance-sensitive code, there is a good reason to spend some time becoming a reindexing ninja: many operations are faster on pre-aligned data. Adding two unaligned DataFrames internally triggers a reindexing step. For exploratory analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact.

Reindexing to align with another object

You may wish to take an object and reindex its axes to be labeled the same as another object. While the syntax for this is straightforward albeit verbose, it is a common enough operation that the reindex_like() method is available to make this simpler:

In [212]: df2
Out[212]: 
        one       two
a  1.394981  1.772517
b  0.343054  1.912123
c  0.695246  1.478369

In [213]: df3
Out[213]: 
        one       two
a  0.583888  0.051514
b -0.468040  0.191120
c -0.115848 -0.242634

In [214]: df.reindex_like(df2)
Out[214]: 
        one       two
a  1.394981  1.772517
b  0.343054  1.912123
c  0.695246  1.478369

Aligning objects with each other with align

The align() method is the fastest way to simultaneously align two objects. It supports a join argument (related to joining and merging):

  • join='outer': take the union of the indexes (default)

  • join='left': use the calling object’s index

  • join='right': use the passed object’s index

  • join='inner': intersect the indexes

It returns a tuple with both of the reindexed Series:

In [215]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

In [216]: s1 = s[:4]

In [217]: s2 = s[1:]

In [218]: s1.align(s2)
Out[218]: 
(a   -0.186646
 b   -1.692424
 c   -0.303893
 d   -1.425662
 e         NaN
 dtype: float64,
 a         NaN
 b   -1.692424
 c   -0.303893
 d   -1.425662
 e    1.114285
 dtype: float64)

In [219]: s1.align(s2, join='inner')
Out[219]: 
(b   -1.692424
 c   -0.303893
 d   -1.425662
 dtype: float64,
 b   -1.692424
 c   -0.303893
 d   -1.425662
 dtype: float64)

In [220]: s1.align(s2, join='left')
Out[220]: 
(a   -0.186646
 b   -1.692424
 c   -0.303893
 d   -1.425662
 dtype: float64,
 a         NaN
 b   -1.692424
 c   -0.303893
 d   -1.425662
 dtype: float64)

For DataFrames, the join method will be applied to both the index and the columns by default:

In [221]: df.align(df2, join='inner')
Out[221]: 
(        one       two
 a  1.394981  1.772517
 b  0.343054  1.912123
 c  0.695246  1.478369,
         one       two
 a  1.394981  1.772517
 b  0.343054  1.912123
 c  0.695246  1.478369)

You can also pass an axis option to only align on the specified axis:

In [222]: df.align(df2, join='inner', axis=0)
Out[222]: 
(        one       two     three
 a  1.394981  1.772517       NaN
 b  0.343054  1.912123 -0.050390
 c  0.695246  1.478369  1.227435,
         one       two
 a  1.394981  1.772517
 b  0.343054  1.912123
 c  0.695246  1.478369)

If you pass a Series to DataFrame.align(), you can choose to align both objects either on the DataFrame’s index or columns using the axis argument:

In [223]: df.align(df2.iloc[0], axis=1)
Out[223]: 
(        one     three       two
 a  1.394981       NaN  1.772517
 b  0.343054 -0.050390  1.912123
 c  0.695246  1.227435  1.478369
 d       NaN -0.613172  0.279344,
 one      1.394981
 three         NaN
 two      1.772517
 Name: a, dtype: float64)

Filling while reindexing

reindex() takes an optional parameter method which is a filling method chosen from the following table:

Method

Action

pad / ffill

Fill values forward

bfill / backfill

Fill values backward

nearest

Fill from the nearest index value

We illustrate these fill methods on a simple Series:

In [224]: rng = pd.date_range('1/3/2000', periods=8)

In [225]: ts = pd.Series(np.random.randn(8), index=rng)

In [226]: ts2 = ts[[0, 3, 6]]

In [227]: ts
Out[227]: 
2000-01-03    0.183051
2000-01-04    0.400528
2000-01-05   -0.015083
2000-01-06    2.395489
2000-01-07    1.414806
2000-01-08    0.118428
2000-01-09    0.733639
2000-01-10   -0.936077
Freq: D, dtype: float64

In [228]: ts2
Out[228]: 
2000-01-03    0.183051
2000-01-06    2.395489
2000-01-09    0.733639
dtype: float64

In [229]: ts2.reindex(ts.index)
Out[229]: 
2000-01-03    0.183051
2000-01-04         NaN
2000-01-05         NaN
2000-01-06    2.395489
2000-01-07         NaN
2000-01-08         NaN
2000-01-09    0.733639
2000-01-10         NaN
Freq: D, dtype: float64

In [230]: ts2.reindex(ts.index, method='ffill')
Out[230]: 
2000-01-03    0.183051
2000-01-04    0.183051
2000-01-05    0.183051
2000-01-06    2.395489
2000-01-07    2.395489
2000-01-08    2.395489
2000-01-09    0.733639
2000-01-10    0.733639
Freq: D, dtype: float64

In [231]: ts2.reindex(ts.index, method='bfill')
Out[231]: 
2000-01-03    0.183051
2000-01-04    2.395489
2000-01-05    2.395489
2000-01-06    2.395489
2000-01-07    0.733639
2000-01-08    0.733639
2000-01-09    0.733639
2000-01-10         NaN
Freq: D, dtype: float64

In [232]: ts2.reindex(ts.index, method='nearest')
Out[232]: 
2000-01-03    0.183051
2000-01-04    0.183051
2000-01-05    2.395489
2000-01-06    2.395489
2000-01-07    2.395489
2000-01-08    0.733639
2000-01-09    0.733639
2000-01-10    0.733639
Freq: D, dtype: float64

These methods require that the indexes are ordered increasing or decreasing.

Note that the same result could have been achieved using fillna (except for method='nearest') or interpolate:

In [233]: ts2.reindex(ts.index).fillna(method='ffill')
Out[233]: 
2000-01-03    0.183051
2000-01-04    0.183051
2000-01-05    0.183051
2000-01-06    2.395489
2000-01-07    2.395489
2000-01-08    2.395489
2000-01-09    0.733639
2000-01-10    0.733639
Freq: D, dtype: float64

reindex() will raise a ValueError if the index is not monotonically increasing or decreasing. fillna() and interpolate() will not perform any checks on the order of the index.

Limits on filling while reindexing

The limit and tolerance arguments provide additional control over filling while reindexing. Limit specifies the maximum count of consecutive matches:

In [234]: ts2.reindex(ts.index, method='ffill', limit=1)
Out[234]: 
2000-01-03    0.183051
2000-01-04    0.183051
2000-01-05         NaN
2000-01-06    2.395489
2000-01-07    2.395489
2000-01-08         NaN
2000-01-09    0.733639
2000-01-10    0.733639
Freq: D, dtype: float64

In contrast, tolerance specifies the maximum distance between the index and indexer values:

In [235]: ts2.reindex(ts.index, method='ffill', tolerance='1 day')
Out[235]: 
2000-01-03    0.183051
2000-01-04    0.183051
2000-01-05         NaN
2000-01-06    2.395489
2000-01-07    2.395489
2000-01-08         NaN
2000-01-09    0.733639
2000-01-10    0.733639
Freq: D, dtype: float64

Notice that when used on a DatetimeIndex, TimedeltaIndex or PeriodIndex, tolerance will coerced into a Timedelta if possible. This allows you to specify tolerance with appropriate strings.

Dropping labels from an axis

A method closely related to reindex is the drop() function. It removes a set of labels from an axis:

In [236]: df
Out[236]: 
        one       two     three
a  1.394981  1.772517       NaN
b  0.343054  1.912123 -0.050390
c  0.695246  1.478369  1.227435
d       NaN  0.279344 -0.613172

In [237]: df.drop(['a', 'd'], axis=0)
Out[237]: 
        one       two     three
b  0.343054  1.912123 -0.050390
c  0.695246  1.478369  1.227435

In [238]: df.drop(['one'], axis=1)
Out[238]: 
        two     three
a  1.772517       NaN
b  1.912123 -0.050390
c  1.478369  1.227435
d  0.279344 -0.613172

Note that the following also works, but is a bit less obvious / clean:

In [239]: df.reindex(df.index.difference(['a', 'd']))
Out[239]: 
        one       two     three
b  0.343054  1.912123 -0.050390
c  0.695246  1.478369  1.227435

Renaming / mapping labels

The rename() method allows you to relabel an axis based on some mapping (a dict or Series) or an arbitrary function.

In [240]: s
Out[240]: 
a   -0.186646
b   -1.692424
c   -0.303893
d   -1.425662
e    1.114285
dtype: float64

In [241]: s.rename(str.upper)
Out[241]: 
A   -0.186646
B   -1.692424
C   -0.303893
D   -1.425662
E    1.114285
dtype: float64

If you pass a function, it must return a value when called with any of the labels (and must produce a set of unique values). A dict or Series can also be used:

In [242]: df.rename(columns={'one': 'foo', 'two': 'bar'},
   .....:           index={'a': 'apple', 'b': 'banana', 'd': 'durian'})
   .....: 
Out[242]: 
             foo       bar     three
apple   1.394981  1.772517       NaN
banana  0.343054  1.912123 -0.050390
c       0.695246  1.478369  1.227435
durian       NaN  0.279344 -0.613172

If the mapping doesn’t include a column/index label, it isn’t renamed. Note that extra labels in the mapping don’t throw an error.

New in version 0.21.0.

DataFrame.rename() also supports an “axis-style” calling convention, where you specify a single mapper and the axis to apply that mapping to.

In [243]: df.rename({'one': 'foo', 'two': 'bar'}, axis='columns')
Out[243]: 
        foo       bar     three
a  1.394981  1.772517       NaN
b  0.343054  1.912123 -0.050390
c  0.695246  1.478369  1.227435
d       NaN  0.279344 -0.613172

In [244]: df.rename({'a': 'apple', 'b': 'banana', 'd': 'durian'}, axis='index')
Out[244]: 
             one       two     three
apple   1.394981  1.772517       NaN
banana  0.343054  1.912123 -0.050390
c       0.695246  1.478369  1.227435
durian       NaN  0.279344 -0.613172

The rename() method also provides an inplace named parameter that is by default False and copies the underlying data. Pass inplace=True to rename the data in place.

Finally, rename() also accepts a scalar or list-like for altering the Series.name attribute.

In [245]: s.rename("scalar-name")
Out[245]: 
a   -0.186646
b   -1.692424
c   -0.303893
d   -1.425662
e    1.114285
Name: scalar-name, dtype: float64

New in version 0.24.0.

The methods rename_axis() and rename_axis() allow specific names of a MultiIndex to be changed (as opposed to the labels).

In [246]: df = pd.DataFrame({'x': [1, 2, 3, 4, 5, 6],
   .....:                    'y': [10, 20, 30, 40, 50, 60]},
   .....:                   index=pd.MultiIndex.from_product([['a', 'b', 'c'], [1, 2]],
   .....:                   names=['let', 'num']))
   .....: 

In [247]: df
Out[247]: 
         x   y
let num       
a   1    1  10
    2    2  20
b   1    3  30
    2    4  40
c   1    5  50
    2    6  60

In [248]: df.rename_axis(index={'let': 'abc'})
Out[248]: 
         x   y
abc num       
a   1    1  10
    2    2  20
b   1    3  30
    2    4  40
c   1    5  50
    2    6  60

In [249]: df.rename_axis(index=str.upper)
Out[249]: 
         x   y
LET NUM       
a   1    1  10
    2    2  20
b   1    3  30
    2    4  40
c   1    5  50
    2    6  60

Iteration

The behavior of basic iteration over pandas objects depends on the type. When iterating over a Series, it is regarded as array-like, and basic iteration produces the values. DataFrames follow the dict-like convention of iterating over the “keys” of the objects.

In short, basic iteration (for i in object) produces:

  • Series: values

  • DataFrame: column labels

Thus, for example, iterating over a DataFrame gives you the column names:

In [250]: df = pd.DataFrame({'col1': np.random.randn(3),
   .....:                    'col2': np.random.randn(3)}, index=['a', 'b', 'c'])
   .....: 

In [251]: for col in df:
   .....:     print(col)
   .....: 
col1
col2

Pandas objects also have the dict-like items() method to iterate over the (key, value) pairs.

To iterate over the rows of a DataFrame, you can use the following methods:

  • iterrows(): Iterate over the rows of a DataFrame as (index, Series) pairs. This converts the rows to Series objects, which can change the dtypes and has some performance implications.

  • itertuples(): Iterate over the rows of a DataFrame as namedtuples of the values. This is a lot faster than iterrows(), and is in most cases preferable to use to iterate over the values of a DataFrame.

Warning

Iterating through pandas objects is generally slow. In many cases, iterating manually over the rows is not needed and can be avoided with one of the following approaches:

  • Look for a vectorized solution: many operations can be performed using built-in methods or NumPy functions, (boolean) indexing, …

  • When you have a function that cannot work on the full DataFrame/Series at once, it is better to use apply() instead of iterating over the values. See the docs on function application.

  • If you need to do iterative manipulations on the values but performance is important, consider writing the inner loop with cython or numba. See the enhancing performance section for some examples of this approach.

Warning

You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect!

For example, in the following case setting the value has no effect:

In [252]: df = pd.DataFrame({'a': [1, 2, 3], 'b': ['a', 'b', 'c']})

In [253]: for index, row in df.iterrows():
   .....:     row['a'] = 10
   .....: 

In [254]: df
Out[254]: 
   a  b
0  1  a
1  2  b
2  3  c

items

Consistent with the dict-like interface, items() iterates through key-value pairs:

  • Series: (index, scalar value) pairs

  • DataFrame: (column, Series) pairs

For example:

In [255]: for label, ser in df.items():
   .....:     print(label)
   .....:     print(ser)
   .....: 
a
0    1
1    2
2    3
Name: a, dtype: int64
b
0    a
1    b
2    c
Name: b, dtype: object

iterrows

iterrows() allows you to iterate through the rows of a DataFrame as Series objects. It returns an iterator yielding each index value along with a Series containing the data in each row:

In [256]: for row_index, row in df.iterrows():
   .....:     print(row_index, row, sep='\n')
   .....: 
0
a    1
b    a
Name: 0, dtype: object
1
a    2
b    b
Name: 1, dtype: object
2
a    3
b    c
Name: 2, dtype: object

Note

Because iterrows() returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,

In [257]: df_orig = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])

In [258]: df_orig.dtypes
Out[258]: 
int        int64
float    float64
dtype: object

In [259]: row = next(df_orig.iterrows())[1]

In [260]: row
Out[260]: 
int      1.0
float    1.5
Name: 0, dtype: float64

All values in row, returned as a Series, are now upcasted to floats, also the original integer value in column x:

In [261]: row['int'].dtype
Out[261]: dtype('float64')

In [262]: df_orig['int'].dtype
Out[262]: dtype('int64')

To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally much faster than iterrows().

For instance, a contrived way to transpose the DataFrame would be:

In [263]: df2 = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})

In [264]: print(df2)
   x  y
0  1  4
1  2  5
2  3  6

In [265]: print(df2.T)
   0  1  2
x  1  2  3
y  4  5  6

In [266]: df2_t = pd.DataFrame({idx: values for idx, values in df2.iterrows()})

In [267]: print(df2_t)
   0  1  2
x  1  2  3
y  4  5  6

itertuples

The itertuples() method will return an iterator yielding a namedtuple for each row in the DataFrame. The first element of the tuple will be the row’s corresponding index value, while the remaining values are the row values.

For instance:

In [268]: for row in df.itertuples():
   .....:     print(row)
   .....: 
Pandas(Index=0, a=1, b='a')
Pandas(Index=1, a=2, b='b')
Pandas(Index=2, a=3, b='c')

This method does not convert the row to a Series object; it merely returns the values inside a namedtuple. Therefore, itertuples() preserves the data type of the values and is generally faster as iterrows().

Note

The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore. With a large number of columns (>255), regular tuples are returned.

.dt accessor

Series has an accessor to succinctly return datetime like properties for the values of the Series, if it is a datetime/period like Series. This will return a Series, indexed like the existing Series.

# datetime
In [269]: s = pd.Series(pd.date_range('20130101 09:10:12', periods=4))

In [270]: s
Out[270]: 
0   2013-01-01 09:10:12
1   2013-01-02 09:10:12
2   2013-01-03 09:10:12
3   2013-01-04 09:10:12
dtype: datetime64[ns]

In [271]: s.dt.hour
Out[271]: 
0    9
1    9
2    9
3    9
dtype: int64

In [272]: s.dt.second
Out[272]: 
0    12
1    12
2    12
3    12
dtype: int64

In [273]: s.dt.day
Out[273]: 
0    1
1    2
2    3
3    4
dtype: int64

This enables nice expressions like this:

In [274]: s[s.dt.day == 2]
Out[274]: 
1   2013-01-02 09:10:12
dtype: datetime64[ns]

You can easily produces tz aware transformations:

In [275]: stz = s.dt.tz_localize('US/Eastern')

In [276]: stz
Out[276]: 
0   2013-01-01 09:10:12-05:00
1   2013-01-02 09:10:12-05:00
2   2013-01-03 09:10:12-05:00
3   2013-01-04 09:10:12-05:00
dtype: datetime64[ns, US/Eastern]

In [277]: stz.dt.tz
Out[277]: <DstTzInfo 'US/Eastern' LMT-1 day, 19:04:00 STD>

You can also chain these types of operations:

In [278]: s.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
Out[278]: 
0   2013-01-01 04:10:12-05:00
1   2013-01-02 04:10:12-05:00
2   2013-01-03 04:10:12-05:00
3   2013-01-04 04:10:12-05:00
dtype: datetime64[ns, US/Eastern]

You can also format datetime values as strings with Series.dt.strftime() which supports the same format as the standard strftime().

# DatetimeIndex
In [279]: s = pd.Series(pd.date_range('20130101', periods=4))

In [280]: s
Out[280]: 
0   2013-01-01
1   2013-01-02
2   2013-01-03
3   2013-01-04
dtype: datetime64[ns]

In [281]: s.dt.strftime('%Y/%m/%d')
Out[281]: 
0    2013/01/01
1    2013/01/02
2    2013/01/03
3    2013/01/04
dtype: object
# PeriodIndex
In [282]: s = pd.Series(pd.period_range('20130101', periods=4))

In [283]: s
Out[283]: 
0    2013-01-01
1    2013-01-02
2    2013-01-03
3    2013-01-04
dtype: period[D]

In [284]: s.dt.strftime('%Y/%m/%d')
Out[284]: 
0    2013/01/01
1    2013/01/02
2    2013/01/03
3    2013/01/04
dtype: object

The .dt accessor works for period and timedelta dtypes.

# period
In [285]: s = pd.Series(pd.period_range('20130101', periods=4, freq='D'))

In [286]: s
Out[286]: 
0    2013-01-01
1    2013-01-02
2    2013-01-03
3    2013-01-04
dtype: period[D]

In [287]: s.dt.year
Out[287]: 
0    2013
1    2013
2    2013
3    2013
dtype: int64

In [288]: s.dt.day
Out[288]: 
0    1
1    2
2    3
3    4
dtype: int64
# timedelta
In [289]: s = pd.Series(pd.timedelta_range('1 day 00:00:05', periods=4, freq='s'))

In [290]: s
Out[290]: 
0   1 days 00:00:05
1   1 days 00:00:06
2   1 days 00:00:07
3   1 days 00:00:08
dtype: timedelta64[ns]

In [291]: s.dt.days
Out[291]: 
0    1
1    1
2    1
3    1
dtype: int64

In [292]: s.dt.seconds
Out[292]: 
0    5
1    6
2    7
3    8
dtype: int64

In [293]: s.dt.components
Out[293]: 
   days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
0     1      0        0        5             0             0            0
1     1      0        0        6             0             0            0
2     1      0        0        7             0             0            0
3     1      0        0        8             0             0            0

Note

Series.dt will raise a TypeError if you access with a non-datetime-like values.

Vectorized string methods

Series is equipped with a set of string processing methods that make it easy to operate on each element of the array. Perhaps most importantly, these methods exclude missing/NA values automatically. These are accessed via the Series’s str attribute and generally have names matching the equivalent (scalar) built-in string methods. For example:

In [294]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'],
   .....:               dtype="string")
   .....: 

In [295]: s.str.lower()
Out[295]: 
0       a
1       b
2       c
3    aaba
4    baca
5    <NA>
6    caba
7     dog
8     cat
dtype: string

Powerful pattern-matching methods are provided as well, but note that pattern-matching generally uses regular expressions by default (and in some cases always uses them).

Note

Prior to pandas 1.0, string methods were only available on object -dtype Series. Pandas 1.0 added the StringDtype which is dedicated to strings. See Text Data Types for more.

Please see Vectorized String Methods for a complete description.

Sorting

Pandas supports three kinds of sorting: sorting by index labels, sorting by column values, and sorting by a combination of both.

By index

The Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels.

In [296]: df = pd.DataFrame({
   .....:     'one': pd.Series(np.random.randn(3), index=['a', 'b', 'c']),
   .....:     'two': pd.Series(np.random.randn(4), index=['a', 'b', 'c', 'd']),
   .....:     'three': pd.Series(np.random.randn(3), index=['b', 'c', 'd'])})
   .....: 

In [297]: unsorted_df = df.reindex(index=['a', 'd', 'c', 'b'],
   .....:                          columns=['three', 'two', 'one'])
   .....: 

In [298]: unsorted_df
Out[298]: 
      three       two       one
a       NaN -1.152244  0.562973
d -0.252916 -0.109597       NaN
c  1.273388 -0.167123  0.640382
b -0.098217  0.009797 -1.299504

# DataFrame
In [299]: unsorted_df.sort_index()
Out[299]: 
      three       two       one
a       NaN -1.152244  0.562973
b -0.098217  0.009797 -1.299504
c  1.273388 -0.167123  0.640382
d -0.252916 -0.109597       NaN

In [300]: unsorted_df.sort_index(ascending=False)
Out[300]: 
      three       two       one
d -0.252916 -0.109597       NaN
c  1.273388 -0.167123  0.640382
b -0.098217  0.009797 -1.299504
a       NaN -1.152244  0.562973

In [301]: unsorted_df.sort_index(axis=1)
Out[301]: 
        one     three       two
a  0.562973       NaN -1.152244
d       NaN -0.252916 -0.109597
c  0.640382  1.273388 -0.167123
b -1.299504 -0.098217  0.009797

# Series
In [302]: unsorted_df['three'].sort_index()
Out[302]: 
a         NaN
b   -0.098217
c    1.273388
d   -0.252916
Name: three, dtype: float64

By values

The Series.sort_values() method is used to sort a Series by its values. The DataFrame.sort_values() method is used to sort a DataFrame by its column or row values. The optional by parameter to DataFrame.sort_values() may used to specify one or more columns to use to determine the sorted order.

In [303]: df1 = pd.DataFrame({'one': [2, 1, 1, 1],
   .....:                     'two': [1, 3, 2, 4],
   .....:                     'three': [5, 4, 3, 2]})
   .....: 

In [304]: df1.sort_values(by='two')
Out[304]: 
   one  two  three
0    2    1      5
2    1    2      3
1    1    3      4
3    1    4      2

The by parameter can take a list of column names, e.g.:

In [305]: df1[['one', 'two', 'three']].sort_values(by=['one', 'two'])
Out[305]: 
   one  two  three
2    1    2      3
1    1    3      4
3    1    4      2
0    2    1      5

These methods have special treatment of NA values via the na_position argument:

In [306]: s[2] = np.nan

In [307]: s.sort_values()
Out[307]: 
0       A
3    Aaba
1       B
4    Baca
6    CABA
8     cat
7     dog
2    <NA>
5    <NA>
dtype: string

In [308]: s.sort_values(na_position='first')
Out[308]: 
2    <NA>
5    <NA>
0       A
3    Aaba
1       B
4    Baca
6    CABA
8     cat
7     dog
dtype: string

By indexes and values

New in version 0.23.0.

Strings passed as the by parameter to DataFrame.sort_values() may refer to either columns or index level names.

# Build MultiIndex
In [309]: idx = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('a', 2),
   .....:                                 ('b', 2), ('b', 1), ('b', 1)])
   .....: 

In [310]: idx.names = ['first', 'second']

# Build DataFrame
In [311]: df_multi = pd.DataFrame({'A': np.arange(6, 0, -1)},
   .....:                         index=idx)
   .....: 

In [312]: df_multi
Out[312]: 
              A
first second   
a     1       6
      2       5
      2       4
b     2       3
      1       2
      1       1

Sort by ‘second’ (index) and ‘A’ (column)

In [313]: df_multi.sort_values(by=['second', 'A'])
Out[313]: 
              A
first second   
b     1       1
      1       2
a     1       6
b     2       3
a     2       4
      2       5

Note

If a string matches both a column name and an index level name then a warning is issued and the column takes precedence. This will result in an ambiguity error in a future version.

searchsorted

Series has the searchsorted() method, which works similarly to numpy.ndarray.searchsorted().

In [314]: ser = pd.Series([1, 2, 3])

In [315]: ser.searchsorted([0, 3])
Out[315]: array([0, 2])

In [316]: ser.searchsorted([0, 4])
Out[316]: array([0, 3])

In [317]: ser.searchsorted([1, 3], side='right')
Out[317]: array([1, 3])

In [318]: ser.searchsorted([1, 3], side='left')
Out[318]: array([0, 2])

In [319]: ser = pd.Series([3, 1, 2])

In [320]: ser.searchsorted([0, 3], sorter=np.argsort(ser))
Out[320]: array([0, 2])

smallest / largest values

Series has the nsmallest() and nlargest() methods which return the smallest or largest \(n\) values. For a large Series this can be much faster than sorting the entire Series and calling head(n) on the result.

In [321]: s = pd.Series(np.random.permutation(10))

In [322]: s
Out[322]: 
0    2
1    0
2    3
3    7
4    1
5    5
6    9
7    6
8    8
9    4
dtype: int64

In [323]: s.sort_values()
Out[323]: 
1    0
4    1
0    2
2    3
9    4
5    5
7    6
3    7
8    8
6    9
dtype: int64

In [324]: s.nsmallest(3)
Out[324]: 
1    0
4    1
0    2
dtype: int64

In [325]: s.nlargest(3)
Out[325]: 
6    9
8    8
3    7
dtype: int64

DataFrame also has the nlargest and nsmallest methods.

In [326]: df = pd.DataFrame({'a': [-2, -1, 1, 10, 8, 11, -1],
   .....:                    'b': list('abdceff'),
   .....:                    'c': [1.0, 2.0, 4.0, 3.2, np.nan, 3.0, 4.0]})
   .....: 

In [327]: df.nlargest(3, 'a')
Out[327]: 
    a  b    c
5  11  f  3.0
3  10  c  3.2
4   8  e  NaN

In [328]: df.nlargest(5, ['a', 'c'])
Out[328]: 
    a  b    c
5  11  f  3.0
3  10  c  3.2
4   8  e  NaN
2   1  d  4.0
6  -1  f  4.0

In [329]: df.nsmallest(3, 'a')
Out[329]: 
   a  b    c
0 -2  a  1.0
1 -1  b  2.0
6 -1  f  4.0

In [330]: df.nsmallest(5, ['a', 'c'])
Out[330]: 
   a  b    c
0 -2  a  1.0
1 -1  b  2.0
6 -1  f  4.0
2  1  d  4.0
4  8  e  NaN

Sorting by a MultiIndex column

You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by.

In [331]: df1.columns = pd.MultiIndex.from_tuples([('a', 'one'),
   .....:                                          ('a', 'two'),
   .....:                                          ('b', 'three')])
   .....: 

In [332]: df1.sort_values(by=('a', 'two'))
Out[332]: 
    a         b
  one two three
0   2   1     5
2   1   2     3
1   1   3     4
3   1   4     2

Copying

The copy() method on pandas objects copies the underlying data (though not the axis indexes, since they are immutable) and returns a new object. Note that it is seldom necessary to copy objects. For example, there are only a handful of ways to alter a DataFrame in-place:

  • Inserting, deleting, or modifying a column.

  • Assigning to the index or columns attributes.

  • For homogeneous data, directly modifying the values via the values attribute or advanced indexing.

To be clear, no pandas method has the side effect of modifying your data; almost every method returns a new object, leaving the original object untouched. If the data is modified, it is because you did so explicitly.

dtypes

For the most part, pandas uses NumPy arrays and dtypes for Series or individual columns of a DataFrame. NumPy provides support for float, int, bool, timedelta64[ns] and datetime64[ns] (note that NumPy does not support timezone-aware datetimes).

Pandas and third-party libraries extend NumPy’s type system in a few places. This section describes the extensions pandas has made internally. See Extension types for how to write your own extension that works with pandas. See Extension data types for a list of third-party libraries that have implemented an extension.

The following table lists all of pandas extension types. For methods requiring dtype arguments, strings can be specified as indicated. See the respective documentation sections for more on each type.

Kind of Data

Data Type

Scalar

Array

String Aliases

Documentation

tz-aware datetime

DatetimeTZDtype

Timestamp

arrays.DatetimeArray

'datetime64[ns, <tz>]'

Time zone handling

Categorical

CategoricalDtype

(none)

Categorical

'category'

Categorical data

period (time spans)

PeriodDtype

Period

arrays.PeriodArray

'period[<freq>]', 'Period[<freq>]'

Time span representation

sparse

SparseDtype

(none)

arrays.SparseArray

'Sparse', 'Sparse[int]', 'Sparse[float]'

Sparse data structures

intervals

IntervalDtype

Interval

arrays.IntervalArray

'interval', 'Interval', 'Interval[<numpy_dtype>]', 'Interval[datetime64[ns, <tz>]]', 'Interval[timedelta64[<freq>]]'

IntervalIndex

nullable integer

Int64Dtype, …

(none)

arrays.IntegerArray

'Int8', 'Int16', 'Int32', 'Int64', 'UInt8', 'UInt16', 'UInt32', 'UInt64'

Nullable integer data type

Strings

StringDtype

str

arrays.StringArray

'string'

Working with text data

Boolean (with NA)

BooleanDtype

bool

arrays.BooleanArray

'boolean'

Boolean data with missing values

Pandas has two ways to store strings.

  1. object dtype, which can hold any Python object, including strings.

  2. StringDtype, which is dedicated to strings.

Generally, we recommend using StringDtype. See Text Data Types fore more.

Finally, arbitrary objects may be stored using the object dtype, but should be avoided to the extent possible (for performance and interoperability with other libraries and methods. See object conversion).

A convenient dtypes attribute for DataFrame returns a Series with the data type of each column.

In [333]: dft = pd.DataFrame({'A': np.random.rand(3),
   .....:                     'B': 1,
   .....:                     'C': 'foo',
   .....:                     'D': pd.Timestamp('20010102'),
   .....:                     'E': pd.Series([1.0] * 3).astype('float32'),
   .....:                     'F': False,
   .....:                     'G': pd.Series([1] * 3, dtype='int8')})
   .....: 

In [334]: dft
Out[334]: 
          A  B    C          D    E      F  G
0  0.035962  1  foo 2001-01-02  1.0  False  1
1  0.701379  1  foo 2001-01-02  1.0  False  1
2  0.281885  1  foo 2001-01-02  1.0  False  1

In [335]: dft.dtypes
Out[335]: 
A           float64
B             int64
C            object
D    datetime64[ns]
E           float32
F              bool
G              int8
dtype: object

On a Series object, use the dtype attribute.

In [336]: dft['A'].dtype
Out[336]: dtype('float64')

If a pandas object contains data with multiple dtypes in a single column, the dtype of the column will be chosen to accommodate all of the data types (object is the most general).

# these ints are coerced to floats
In [337]: pd.Series([1, 2, 3, 4, 5, 6.])
Out[337]: 
0    1.0
1    2.0
2    3.0
3    4.0
4    5.0
5    6.0
dtype: float64

# string data forces an ``object`` dtype
In [338]: pd.Series([1, 2, 3, 6., 'foo'])
Out[338]: 
0      1
1      2
2      3
3      6
4    foo
dtype: object

The number of columns of each type in a DataFrame can be found by calling DataFrame.dtypes.value_counts().

In [339]: dft.dtypes.value_counts()
Out[339]: 
int8              1
int64             1
bool              1
datetime64[ns]    1
float32           1
float64           1
object            1
dtype: int64

Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passed (either directly via the dtype keyword, a passed ndarray, or a passed Series), then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will NOT be combined. The following example will give you a taste.

In [340]: df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')

In [341]: df1
Out[341]: 
          A
0  0.224364
1  1.890546
2  0.182879
3  0.787847
4 -0.188449
5  0.667715
6 -0.011736
7 -0.399073

In [342]: df1.dtypes
Out[342]: 
A    float32
dtype: object

In [343]: df2 = pd.DataFrame({'A': pd.Series(np.random.randn(8), dtype='float16'),
   .....:                     'B': pd.Series(np.random.randn(8)),
   .....:                     'C': pd.Series(np.array(np.random.randn(8),
   .....:                                             dtype='uint8'))})
   .....: 

In [344]: df2
Out[344]: 
          A         B    C
0  0.823242  0.256090    0
1  1.607422  1.426469    0
2 -0.333740 -0.416203  255
3 -0.063477  1.139976    0
4 -1.014648 -1.193477    0
5  0.678711  0.096706    0
6 -0.040863 -1.956850    1
7 -0.357422 -0.714337    0

In [345]: df2.dtypes
Out[345]: 
A    float16
B    float64
C      uint8
dtype: object

defaults

By default integer types are int64 and float types are float64, regardless of platform (32-bit or 64-bit). The following will all result in int64 dtypes.

In [346]: pd.DataFrame([1, 2], columns=['a']).dtypes
Out[346]: 
a    int64
dtype: object

In [347]: pd.DataFrame({'a': [1, 2]}).dtypes
Out[347]: 
a    int64
dtype: object

In [348]: pd.DataFrame({'a': 1}, index=list(range(2))).dtypes
Out[348]: 
a    int64
dtype: object

Note that Numpy will choose platform-dependent types when creating arrays. The following WILL result in int32 on 32-bit platform.

In [349]: frame = pd.DataFrame(np.array([1, 2]))

upcasting

Types can potentially be upcasted when combined with other types, meaning they are promoted from the current type (e.g. int to float).

In [350]: df3 = df1.reindex_like(df2).fillna(value=0.0) + df2

In [351]: df3
Out[351]: 
          A         B      C
0  1.047606  0.256090    0.0
1  3.497968  1.426469    0.0
2 -0.150862 -0.416203  255.0
3  0.724370  1.139976    0.0
4 -1.203098 -1.193477    0.0
5  1.346426  0.096706    0.0
6 -0.052599 -1.956850    1.0
7 -0.756495 -0.714337    0.0

In [352]: df3.dtypes
Out[352]: 
A    float32
B    float64
C    float64
dtype: object

DataFrame.to_numpy() will return the lower-common-denominator of the dtypes, meaning the dtype that can accommodate ALL of the types in the resulting homogeneous dtyped NumPy array. This can force some upcasting.

In [353]: df3.to_numpy().dtype
Out[353]: dtype('float64')

astype

You can use the astype() method to explicitly convert dtypes from one to another. These will by default return a copy, even if the dtype was unchanged (pass copy=False to change this behavior). In addition, they will raise an exception if the astype operation is invalid.

Upcasting is always according to the numpy rules. If two different dtypes are involved in an operation, then the more general one will be used as the result of the operation.

In [354]: df3
Out[354]: 
          A         B      C
0  1.047606  0.256090    0.0
1  3.497968  1.426469    0.0
2 -0.150862 -0.416203  255.0
3  0.724370  1.139976    0.0
4 -1.203098 -1.193477    0.0
5  1.346426  0.096706    0.0
6 -0.052599 -1.956850    1.0
7 -0.756495 -0.714337    0.0

In [355]: df3.dtypes
Out[355]: 
A    float32
B    float64
C    float64
dtype: object

# conversion of dtypes
In [356]: df3.astype('float32').dtypes
Out[356]: 
A    float32
B    float32
C    float32
dtype: object

Convert a subset of columns to a specified type using astype().

In [357]: dft = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})

In [358]: dft[['a', 'b']] = dft[['a', 'b']].astype(np.uint8)

In [359]: dft
Out[359]: 
   a  b  c
0  1  4  7
1  2  5  8
2  3  6  9

In [360]: dft.dtypes
Out[360]: 
a    uint8
b    uint8
c    int64
dtype: object

Convert certain columns to a specific dtype by passing a dict to astype().

In [361]: dft1 = pd.DataFrame({'a': [1, 0, 1], 'b': [4, 5, 6], 'c': [7, 8, 9]})

In [362]: dft1 = dft1.astype({'a': np.bool, 'c': np.float64})

In [363]: dft1
Out[363]: 
       a  b    c
0   True  4  7.0
1  False  5  8.0
2   True  6  9.0

In [364]: dft1.dtypes
Out[364]: 
a       bool
b      int64
c    float64
dtype: object

Note

When trying to convert a subset of columns to a specified type using astype() and loc(), upcasting occurs.

loc() tries to fit in what we are assigning to the current dtypes, while [] will overwrite them taking the dtype from the right hand side. Therefore the following piece of code produces the unintended result.

In [365]: dft = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})

In [366]: dft.loc[:, ['a', 'b']].astype(np.uint8).dtypes
Out[366]: 
a    uint8
b    uint8
dtype: object

In [367]: dft.loc[:, ['a', 'b']] = dft.loc[:, ['a', 'b']].astype(np.uint8)

In [368]: dft.dtypes
Out[368]: 
a    int64
b    int64
c    int64
dtype: object

object conversion

pandas offers various functions to try to force conversion of types from the object dtype to other types. In cases where the data is already of the correct type, but stored in an object array, the DataFrame.infer_objects() and Series.infer_objects() methods can be used to soft convert to the correct type.

In [369]: import datetime

In [370]: df = pd.DataFrame([[1, 2],
   .....:                    ['a', 'b'],
   .....:                    [datetime.datetime(2016, 3, 2),
   .....:                     datetime.datetime(2016, 3, 2)]])
   .....: 

In [371]: df = df.T

In [372]: df
Out[372]: 
   0  1          2
0  1  a 2016-03-02
1  2  b 2016-03-02

In [373]: df.dtypes
Out[373]: 
0            object
1            object
2    datetime64[ns]
dtype: object

Because the data was transposed the original inference stored all columns as object, which infer_objects will correct.

In [374]: df.infer_objects().dtypes
Out[374]: 
0             int64
1            object
2    datetime64[ns]
dtype: object

The following functions are available for one dimensional object arrays or scalars to perform hard conversion of objects to a specified type:

  • to_numeric() (conversion to numeric dtypes)

    In [375]: m = ['1.1', 2, 3]
    
    In [376]: pd.to_numeric(m)
    Out[376]: array([1.1, 2. , 3. ])
    
  • to_datetime() (conversion to datetime objects)

    In [377]: import datetime
    
    In [378]: m = ['2016-07-09', datetime.datetime(2016, 3, 2)]
    
    In [379]: pd.to_datetime(m)
    Out[379]: DatetimeIndex(['2016-07-09', '2016-03-02'], dtype='datetime64[ns]', freq=None)
    
  • to_timedelta() (conversion to timedelta objects)

    In [380]: m = ['5us', pd.Timedelta('1day')]
    
    In [381]: pd.to_timedelta(m)
    Out[381]: TimedeltaIndex(['0 days 00:00:00.000005', '1 days 00:00:00'], dtype='timedelta64[ns]', freq=None)
    

To force a conversion, we can pass in an errors argument, which specifies how pandas should deal with elements that cannot be converted to desired dtype or object. By default, errors='raise', meaning that any errors encountered will be raised during the conversion process. However, if errors='coerce', these errors will be ignored and pandas will convert problematic elements to pd.NaT (for datetime and timedelta) or np.nan (for numeric). This might be useful if you are reading in data which is mostly of the desired dtype (e.g. numeric, datetime), but occasionally has non-conforming elements intermixed that you want to represent as missing:

In [382]: import datetime

In [383]: m = ['apple', datetime.datetime(2016, 3, 2)]

In [384]: pd.to_datetime(m, errors='coerce')
Out[384]: DatetimeIndex(['NaT', '2016-03-02'], dtype='datetime64[ns]', freq=None)

In [385]: m = ['apple', 2, 3]

In [386]: pd.to_numeric(m, errors='coerce')
Out[386]: array([nan,  2.,  3.])

In [387]: m = ['apple', pd.Timedelta('1day')]

In [388]: pd.to_timedelta(m, errors='coerce')
Out[388]: TimedeltaIndex([NaT, '1 days'], dtype='timedelta64[ns]', freq=None)

The errors parameter has a third option of errors='ignore', which will simply return the passed in data if it encounters any errors with the conversion to a desired data type:

In [389]: import datetime

In [390]: m = ['apple', datetime.datetime(2016, 3, 2)]

In [391]: pd.to_datetime(m, errors='ignore')
Out[391]: Index(['apple', 2016-03-02 00:00:00], dtype='object')

In [392]: m = ['apple', 2, 3]

In [393]: pd.to_numeric(m, errors='ignore')
Out[393]: array(['apple', 2, 3], dtype=object)

In [394]: m = ['apple', pd.Timedelta('1day')]

In [395]: pd.to_timedelta(m, errors='ignore')
Out[395]: array(['apple', Timedelta('1 days 00:00:00')], dtype=object)

In addition to object conversion, to_numeric() provides another argument downcast, which gives the option of downcasting the newly (or already) numeric data to a smaller dtype, which can conserve memory:

In [396]: m = ['1', 2, 3]

In [397]: pd.to_numeric(m, downcast='integer')   # smallest signed int dtype
Out[397]: array([1, 2, 3], dtype=int8)

In [398]: pd.to_numeric(m, downcast='signed')    # same as 'integer'
Out[398]: array([1, 2, 3], dtype=int8)

In [399]: pd.to_numeric(m, downcast='unsigned')  # smallest unsigned int dtype
Out[399]: array([1, 2, 3], dtype=uint8)

In [400]: pd.to_numeric(m, downcast='float')     # smallest float dtype
Out[400]: array([1., 2., 3.], dtype=float32)

As these methods apply only to one-dimensional arrays, lists or scalars; they cannot be used directly on multi-dimensional objects such as DataFrames. However, with apply(), we can “apply” the function over each column efficiently:

In [401]: import datetime

In [402]: df = pd.DataFrame([
   .....:     ['2016-07-09', datetime.datetime(2016, 3, 2)]] * 2, dtype='O')
   .....: 

In [403]: df
Out[403]: 
            0                    1
0  2016-07-09  2016-03-02 00:00:00
1  2016-07-09  2016-03-02 00:00:00

In [404]: df.apply(pd.to_datetime)
Out[404]: 
           0          1
0 2016-07-09 2016-03-02
1 2016-07-09 2016-03-02

In [405]: df = pd.DataFrame([['1.1', 2, 3]] * 2, dtype='O')

In [406]: df
Out[406]: 
     0  1  2
0  1.1  2  3
1  1.1  2  3

In [407]: df.apply(pd.to_numeric)
Out[407]: 
     0  1  2
0  1.1  2  3
1  1.1  2  3

In [408]: df = pd.DataFrame([['5us', pd.Timedelta('1day')]] * 2, dtype='O')

In [409]: df
Out[409]: 
     0                1
0  5us  1 days 00:00:00
1  5us  1 days 00:00:00

In [410]: df.apply(pd.to_timedelta)
Out[410]: 
                0      1
0 00:00:00.000005 1 days
1 00:00:00.000005 1 days

gotchas

Performing selection operations on integer type data can easily upcast the data to floating. The dtype of the input data will be preserved in cases where nans are not introduced. See also Support for integer NA.

In [411]: dfi = df3.astype('int32')

In [412]: dfi['E'] = 1

In [413]: dfi
Out[413]: 
   A  B    C  E
0  1  0    0  1
1  3  1    0  1
2  0  0  255  1
3  0  1    0  1
4 -1 -1    0  1
5  1  0    0  1
6  0 -1    1  1
7  0  0    0  1

In [414]: dfi.dtypes
Out[414]: 
A    int32
B    int32
C    int32
E    int64
dtype: object

In [415]: casted = dfi[dfi > 0]

In [416]: casted
Out[416]: 
     A    B      C  E
0  1.0  NaN    NaN  1
1  3.0  1.0    NaN  1
2  NaN  NaN  255.0  1
3  NaN  1.0    NaN  1
4  NaN  NaN    NaN  1
5  1.0  NaN    NaN  1
6  NaN  NaN    1.0  1
7  NaN  NaN    NaN  1

In [417]: casted.dtypes
Out[417]: 
A    float64
B    float64
C    float64
E      int64
dtype: object

While float dtypes are unchanged.

In [418]: dfa = df3.copy()

In [419]: dfa['A'] = dfa['A'].astype('float32')

In [420]: dfa.dtypes
Out[420]: 
A    float32
B    float64
C    float64
dtype: object

In [421]: casted = dfa[df2 > 0]

In [422]: casted
Out[422]: 
          A         B      C
0  1.047606  0.256090    NaN
1  3.497968  1.426469    NaN
2       NaN       NaN  255.0
3       NaN  1.139976    NaN
4       NaN       NaN    NaN
5  1.346426  0.096706    NaN
6       NaN       NaN    1.0
7       NaN       NaN    NaN

In [423]: casted.dtypes
Out[423]: 
A    float32
B    float64
C    float64
dtype: object

Selecting columns based on dtype

The select_dtypes() method implements subsetting of columns based on their dtype.

First, let’s create a DataFrame with a slew of different dtypes:

In [424]: df = pd.DataFrame({'string': list('abc'),
   .....:                    'int64': list(range(1, 4)),
   .....:                    'uint8': np.arange(3, 6).astype('u1'),
   .....:                    'float64': np.arange(4.0, 7.0),
   .....:                    'bool1': [True, False, True],
   .....:                    'bool2': [False, True, False],
   .....:                    'dates': pd.date_range('now', periods=3),
   .....:                    'category': pd.Series(list("ABC")).astype('category')})
   .....: 

In [425]: df['tdeltas'] = df.dates.diff()

In [426]: df['uint64'] = np.arange(3, 6).astype('u8')

In [427]: df['other_dates'] = pd.date_range('20130101', periods=3)

In [428]: df['tz_aware_dates'] = pd.date_range('20130101', periods=3, tz='US/Eastern')

In [429]: df
Out[429]: 
  string  int64  uint8  float64  bool1  bool2                      dates category tdeltas  uint64 other_dates            tz_aware_dates
0      a      1      3      4.0   True  False 2020-02-05 17:36:57.948260        A     NaT       3  2013-01-01 2013-01-01 00:00:00-05:00
1      b      2      4      5.0  False   True 2020-02-06 17:36:57.948260        B  1 days       4  2013-01-02 2013-01-02 00:00:00-05:00
2      c      3      5      6.0   True  False 2020-02-07 17:36:57.948260        C  1 days       5  2013-01-03 2013-01-03 00:00:00-05:00

And the dtypes:

In [430]: df.dtypes
Out[430]: 
string                                object
int64                                  int64
uint8                                  uint8
float64                              float64
bool1                                   bool
bool2                                   bool
dates                         datetime64[ns]
category                            category
tdeltas                      timedelta64[ns]
uint64                                uint64
other_dates                   datetime64[ns]
tz_aware_dates    datetime64[ns, US/Eastern]
dtype: object

select_dtypes() has two parameters include and exclude that allow you to say “give me the columns with these dtypes” (include) and/or “give the columns without these dtypes” (exclude).

For example, to select bool columns:

In [431]: df.select_dtypes(include=[bool])
Out[431]: 
   bool1  bool2
0   True  False
1  False   True
2   True  False

You can also pass the name of a dtype in the NumPy dtype hierarchy:

In [432]: df.select_dtypes(include=['bool'])
Out[432]: 
   bool1  bool2
0   True  False
1  False   True
2   True  False

select_dtypes() also works with generic dtypes as well.

For example, to select all numeric and boolean columns while excluding unsigned integers:

In [433]: df.select_dtypes(include=['number', 'bool'], exclude=['unsignedinteger'])
Out[433]: 
   int64  float64  bool1  bool2 tdeltas
0      1      4.0   True  False     NaT
1      2      5.0  False   True  1 days
2      3      6.0   True  False  1 days

To select string columns you must use the object dtype:

In [434]: df.select_dtypes(include=['object'])
Out[434]: 
  string
0      a
1      b
2      c

To see all the child dtypes of a generic dtype like numpy.number you can define a function that returns a tree of child dtypes:

In [435]: def subdtypes(dtype):
   .....:     subs = dtype.__subclasses__()
   .....:     if not subs:
   .....:         return dtype
   .....:     return [dtype, [subdtypes(dt) for dt in subs]]
   .....: 

All NumPy dtypes are subclasses of numpy.generic:

In [436]: subdtypes(np.generic)
Out[436]: 
[numpy.generic,
 [[numpy.number,
   [[numpy.integer,
     [[numpy.signedinteger,
       [numpy.int8,
        numpy.int16,
        numpy.int32,
        numpy.int64,
        numpy.longlong,
        numpy.timedelta64]],
      [numpy.unsignedinteger,
       [numpy.uint8,
        numpy.uint16,
        numpy.uint32,
        numpy.uint64,
        numpy.ulonglong]]]],
    [numpy.inexact,
     [[numpy.floating,
       [numpy.float16, numpy.float32, numpy.float64, numpy.float128]],
      [numpy.complexfloating,
       [numpy.complex64, numpy.complex128, numpy.complex256]]]]]],
  [numpy.flexible,
   [[numpy.character, [numpy.bytes_, numpy.str_]],
    [numpy.void, [numpy.record]]]],
  numpy.bool_,
  numpy.datetime64,
  numpy.object_]]

Note

Pandas also defines the types category, and datetime64[ns, tz], which are not integrated into the normal NumPy hierarchy and won’t show up with the above function.