Here we discuss a lot of the essential functionality common to the pandas data structures. To begin, let’s create some example objects like we did in the 10 minutes to pandas 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']) ...:
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.
head()
tail()
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
pandas objects have a number of attributes enabling you to access the metadata
shape: gives the axis dimensions of the object, consistent with ndarray
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).
Index
Series
DataFrame
numpy.ndarray
To get the actual data inside a Index or Series, use the .array property
.array
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 are a bit beyond the scope of this introduction. See dtypes for more.
array
ExtensionArray
If you know you need a NumPy array, use to_numpy() or numpy.asarray().
to_numpy()
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:
dtype
An object-dtype numpy.ndarray with Timestamp objects, each with the correct tz
Timestamp
tz
A datetime64[ns] -dtype numpy.ndarray, where the values have been converted to UTC and the timezone discarded
datetime64[ns]
Timezones may be preserved with dtype=object
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]'
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:
DataFrame.to_numpy()
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:
Series.values
DataFrame.values
.values
.to_numpy()
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.
Series.array
Series.to_numpy()
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.
pandas has support for accelerating certain types of binary numerical and boolean operations using the numexpr library and the bottleneck libraries.
numexpr
bottleneck
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.
nans
Here is a sample (using 100 column x 100,000 row DataFrames):
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)
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.
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:
add()
sub()
mul()
div()
radd()
rsub()
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:
divmod()
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
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).
fillna
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
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:
eq
ne
lt
gt
le
ge
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.
bool
boolean
You can apply the reductions: empty, any(), all(), and bool() to provide a way to summarize a boolean result.
empty
any()
all()
bool()
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.
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:
df + df
df * 2
(df + df == df * 2).all()
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:
df + df == df * 2
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.
equals()
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
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
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:
combine_first()
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
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).
DataFrame.combine()
So, for instance, to reproduce combine_first() as above:
In [76]: def combiner(x, y): ....: return np.where(pd.isna(x), y, x) ....:
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:
sum()
mean()
quantile()
cumsum()
cumprod()
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):
skipna
True
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 of 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.
NaN
expanding()
rolling()
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.
level
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:
Series.nunique()
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
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):
describe()
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:
include
exclude
all
In [104]: frame.describe(include=['object']) Out[104]: a count 4 unique 2 top Yes 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 Yes 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.
The idxmin() and idxmax() functions on Series and DataFrame compute the index labels with the minimum and maximum corresponding values:
idxmin()
idxmax()
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'
idxmin and idxmax are called argmin and argmax in NumPy.
idxmin
idxmax
argmin
argmax
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:
value_counts()
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
New in version 1.1.0.
The value_counts() method can be used to count combinations across multiple columns. By default all columns are used but a subset can be selected using the subset argument.
subset
In [122]: data = {"a": [1, 2, 3, 4], "b": ["x", "x", "y", "y"]} In [123]: frame = pd.DataFrame(data) In [124]: frame.value_counts() Out[124]: a b 4 y 1 3 y 1 2 x 1 1 x 1 dtype: int64
Similarly, you can get the most frequently occurring value(s), i.e. the mode, of the values in a Series or DataFrame:
In [125]: s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7]) In [126]: s5.mode() Out[126]: 0 3 1 7 dtype: int64 In [127]: df5 = pd.DataFrame({"A": np.random.randint(0, 7, size=50), .....: "B": np.random.randint(-10, 15, size=50)}) .....: In [128]: df5.mode() Out[128]: A B 0 1.0 -9 1 NaN 10 2 NaN 13
Continuous values can be discretized using the cut() (bins based on values) and qcut() (bins based on sample quantiles) functions:
cut()
qcut()
In [129]: arr = np.random.randn(20) In [130]: factor = pd.cut(arr, 4) In [131]: factor Out[131]: [(-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 [132]: factor = pd.cut(arr, [-5, -1, 0, 1, 5]) In [133]: factor Out[133]: [(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 [134]: arr = np.random.randn(30) In [135]: factor = pd.qcut(arr, [0, .25, .5, .75, 1]) In [136]: factor Out[136]: [(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 [137]: pd.value_counts(factor) Out[137]: (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 [138]: arr = np.random.randn(20) In [139]: factor = pd.cut(arr, [-np.inf, 0, np.inf]) In [140]: factor Out[140]: [(-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]]
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.
Tablewise Function Application: pipe()
pipe()
Row or Column-wise Function Application: apply()
apply()
Aggregation API: agg() and transform()
agg()
transform()
Applying Elementwise Functions: applymap()
applymap()
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 [141]: 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 [142]: 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 [143]: df_p = pd.DataFrame({'city_and_code': ['Chicago, IL']})
extract_city_name and add_country_name are functions taking and returning DataFrames.
extract_city_name
add_country_name
Now compare the following:
In [144]: add_country_name(extract_city_name(df_p), country_name='US') Out[144]: city_and_code city_name city_and_country 0 Chicago, IL Chicago ChicagoUS
Is equivalent to:
In [145]: (df_p.pipe(extract_city_name) .....: .pipe(add_country_name, country_name="US")) .....: Out[145]: 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.
pipe
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.
(callable, data_keyword)
.pipe
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:
data
(sm.ols, 'data')
In [146]: import statsmodels.formula.api as sm In [147]: bb = pd.read_csv('data/baseball.csv', index_col='id') In [148]: (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[148]: <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: Fri, 30 Oct 2020 Prob (F-statistic): 3.48e-15 Time: 14:14:44 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 ============================================================================== Notes: [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().
(%>%)
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:
axis
In [149]: df.apply(np.mean) Out[149]: one 0.811094 two 1.360588 three 0.187958 dtype: float64 In [150]: df.apply(np.mean, axis=1) Out[150]: a 1.583749 b 0.734929 c 1.133683 d -0.166914 dtype: float64 In [151]: df.apply(lambda x: x.max() - x.min()) Out[151]: one 1.051928 two 1.632779 three 1.840607 dtype: float64 In [152]: df.apply(np.cumsum) Out[152]: 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 [153]: df.apply(np.exp) Out[153]: 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 [154]: df.apply('mean') Out[154]: one 0.811094 two 1.360588 three 0.187958 dtype: float64 In [155]: df.apply('mean', axis=1) Out[155]: 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:
DataFrame.apply
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.
result_type
reduce
broadcast
expand
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 [156]: tsdf = pd.DataFrame(np.random.randn(1000, 3), columns=['A', 'B', 'C'], .....: index=pd.date_range('1/1/2000', periods=1000)) .....: In [157]: tsdf.apply(lambda x: x.idxmax()) Out[157]: 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 [158]: tsdf Out[158]: 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 [159]: tsdf.apply(pd.Series.interpolate) Out[159]: 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.
raw
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().
DataFrame.aggregate()
DataFrame.agg()
We will use a similar starting frame from above:
In [160]: tsdf = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'], .....: index=pd.date_range('1/1/2000', periods=10)) .....: In [161]: tsdf.iloc[3:7] = np.nan In [162]: tsdf Out[162]: 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 [163]: tsdf.agg(np.sum) Out[163]: A 3.033606 B -1.803879 C 1.575510 dtype: float64 In [164]: tsdf.agg('sum') Out[164]: 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 [165]: tsdf.sum() Out[165]: A 3.033606 B -1.803879 C 1.575510 dtype: float64
Single aggregations on a Series this will return a scalar value:
In [166]: tsdf['A'].agg('sum') Out[166]: 3.033606102414146
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 [167]: tsdf.agg(['sum']) Out[167]: A B C sum 3.033606 -1.803879 1.57551
Multiple functions yield multiple rows:
In [168]: tsdf.agg(['sum', 'mean']) Out[168]: 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 [169]: tsdf['A'].agg(['sum', 'mean']) Out[169]: sum 3.033606 mean 0.505601 Name: A, dtype: float64
Passing a lambda function will yield a <lambda> named row:
lambda
<lambda>
In [170]: tsdf['A'].agg(['sum', lambda x: x.mean()]) Out[170]: sum 3.033606 <lambda> 0.505601 Name: A, dtype: float64
Passing a named function will yield that name for the row:
In [171]: def mymean(x): .....: return x.mean() .....: In [172]: tsdf['A'].agg(['sum', mymean]) Out[172]: sum 3.033606 mymean 0.505601 Name: A, dtype: float64
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.
DataFrame.agg
OrderedDict
In [173]: tsdf.agg({'A': 'mean', 'B': 'sum'}) Out[173]: 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 [174]: tsdf.agg({'A': ['mean', 'min'], 'B': 'sum'}) Out[174]: A B mean 0.505601 NaN min -0.749892 NaN sum NaN -1.803879
When presented with mixed dtypes that cannot aggregate, .agg will only take the valid aggregations. This is similar to how .groupby.agg works.
.agg
.groupby.agg
In [175]: mdf = pd.DataFrame({'A': [1, 2, 3], .....: 'B': [1., 2., 3.], .....: 'C': ['foo', 'bar', 'baz'], .....: 'D': pd.date_range('20130101', periods=3)}) .....: In [176]: mdf.dtypes Out[176]: A int64 B float64 C object D datetime64[ns] dtype: object
In [177]: mdf.agg(['min', 'sum']) Out[177]: A B C D min 1 1.0 bar 2013-01-01 sum 6 6.0 foobarbaz NaT
With .agg() it is possible to easily create a custom describe function, similar to the built in describe function.
.agg()
In [178]: from functools import partial In [179]: q_25 = partial(pd.Series.quantile, q=0.25) In [180]: q_25.__name__ = '25%' In [181]: q_75 = partial(pd.Series.quantile, q=0.75) In [182]: q_75.__name__ = '75%' In [183]: tsdf.agg(['count', 'mean', 'std', 'min', q_25, 'median', q_75, 'max']) Out[183]: 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
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 [184]: tsdf = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'], .....: index=pd.date_range('1/1/2000', periods=10)) .....: In [185]: tsdf.iloc[3:7] = np.nan In [186]: tsdf 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
Transform the entire frame. .transform() allows input functions as: a NumPy function, a string function name or a user defined function.
.transform()
In [187]: tsdf.transform(np.abs) 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 In [188]: tsdf.transform('abs') Out[188]: 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 [189]: tsdf.transform(lambda x: x.abs()) Out[189]: 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 [190]: np.abs(tsdf) Out[190]: 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 [191]: tsdf['A'].transform(np.abs) Out[191]: 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
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 [192]: tsdf.transform([np.abs, lambda x: x + 1]) Out[192]: 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 [193]: tsdf['A'].transform([np.abs, lambda x: x + 1]) Out[193]: 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
Passing a dict of functions will allow selective transforming per column.
In [194]: tsdf.transform({'A': np.abs, 'B': lambda x: x + 1}) Out[194]: 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 [195]: tsdf.transform({'A': np.abs, 'B': [lambda x: x + 1, 'sqrt']}) Out[195]: 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
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:
map()
In [196]: df4 Out[196]: 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 [197]: def f(x): .....: return len(str(x)) .....: In [198]: df4['one'].map(f) Out[198]: a 18 b 19 c 18 d 3 Name: one, dtype: int64 In [199]: df4.applymap(f) Out[199]: 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:
Series.map()
In [200]: s = pd.Series(['six', 'seven', 'six', 'seven', 'six'], .....: index=['a', 'b', 'c', 'd', 'e']) .....: In [201]: t = pd.Series({'six': 6., 'seven': 7.}) In [202]: s Out[202]: a six b seven c six d seven e six dtype: object In [203]: s.map(t) Out[203]: a 6.0 b 7.0 c 6.0 d 7.0 e 6.0 dtype: float64
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:
reindex()
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 [204]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e']) In [205]: s Out[205]: a 1.695148 b 1.328614 c 1.234686 d -0.385845 e -1.326508 dtype: float64 In [206]: s.reindex(['e', 'b', 'f', 'd']) Out[206]: 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.
f
With a DataFrame, you can simultaneously reindex the index and columns:
In [207]: df Out[207]: 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 [208]: df.reindex(index=['c', 'f', 'b'], columns=['three', 'two', 'one']) Out[208]: 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:
reindex
In [209]: df.reindex(['c', 'f', 'b'], axis='index') Out[209]: 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 [210]: rs = s.reindex(df.index) In [211]: rs Out[211]: a 1.695148 b 1.328614 c 1.234686 d -0.385845 dtype: float64 In [212]: rs.index is df.index Out[212]: True
This means that the reindexed Series’s index is the same Python object as the DataFrame’s index.
DataFrame.reindex() also supports an “axis-style” calling convention, where you specify a single labels argument and the axis it applies to.
DataFrame.reindex()
labels
In [213]: df.reindex(['c', 'f', 'b'], axis='index') Out[213]: one two three c 0.695246 1.478369 1.227435 f NaN NaN NaN b 0.343054 1.912123 -0.050390 In [214]: df.reindex(['three', 'two', 'one'], axis='columns') Out[214]: 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.
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.
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:
reindex_like()
In [215]: df2 Out[215]: one two a 1.394981 1.772517 b 0.343054 1.912123 c 0.695246 1.478369 In [216]: df3 Out[216]: one two a 0.583888 0.051514 b -0.468040 0.191120 c -0.115848 -0.242634 In [217]: df.reindex_like(df2) Out[217]: one two a 1.394981 1.772517 b 0.343054 1.912123 c 0.695246 1.478369
align
The align() method is the fastest way to simultaneously align two objects. It supports a join argument (related to joining and merging):
align()
join
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
join='outer': take the union of the indexes (default)
join='outer'
join='left': use the calling object’s index
join='left'
join='right': use the passed object’s index
join='right'
join='inner': intersect the indexes
join='inner'
It returns a tuple with both of the reindexed Series:
In [218]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e']) In [219]: s1 = s[:4] In [220]: s2 = s[1:] In [221]: s1.align(s2) Out[221]: (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 [222]: s1.align(s2, join='inner') Out[222]: (b -1.692424 c -0.303893 d -1.425662 dtype: float64, b -1.692424 c -0.303893 d -1.425662 dtype: float64) In [223]: s1.align(s2, join='left') Out[223]: (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 [224]: df.align(df2, join='inner') Out[224]: ( 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 [225]: df.align(df2, join='inner', axis=0) Out[225]: ( 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:
DataFrame.align()
In [226]: df.align(df2.iloc[0], axis=1) Out[226]: ( 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)
reindex() takes an optional parameter method which is a filling method chosen from the following table:
method
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 [227]: rng = pd.date_range('1/3/2000', periods=8) In [228]: ts = pd.Series(np.random.randn(8), index=rng) In [229]: ts2 = ts[[0, 3, 6]] In [230]: ts Out[230]: 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 [231]: ts2 Out[231]: 2000-01-03 0.183051 2000-01-06 2.395489 2000-01-09 0.733639 Freq: 3D, dtype: float64 In [232]: ts2.reindex(ts.index) Out[232]: 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 [233]: ts2.reindex(ts.index, 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 In [234]: ts2.reindex(ts.index, method='bfill') Out[234]: 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 [235]: ts2.reindex(ts.index, method='nearest') Out[235]: 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:
method='nearest'
In [236]: ts2.reindex(ts.index).fillna(method='ffill') Out[236]: 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.
fillna()
interpolate()
The limit and tolerance arguments provide additional control over filling while reindexing. Limit specifies the maximum count of consecutive matches:
limit
tolerance
In [237]: ts2.reindex(ts.index, method='ffill', limit=1) Out[237]: 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 [238]: ts2.reindex(ts.index, method='ffill', tolerance='1 day') Out[238]: 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.
DatetimeIndex
TimedeltaIndex
PeriodIndex
Timedelta
A method closely related to reindex is the drop() function. It removes a set of labels from an axis:
drop()
In [239]: df Out[239]: 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 [240]: df.drop(['a', 'd'], axis=0) Out[240]: one two three b 0.343054 1.912123 -0.050390 c 0.695246 1.478369 1.227435 In [241]: df.drop(['one'], axis=1) Out[241]: 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 [242]: df.reindex(df.index.difference(['a', 'd'])) Out[242]: one two three b 0.343054 1.912123 -0.050390 c 0.695246 1.478369 1.227435
The rename() method allows you to relabel an axis based on some mapping (a dict or Series) or an arbitrary function.
rename()
In [243]: s Out[243]: a -0.186646 b -1.692424 c -0.303893 d -1.425662 e 1.114285 dtype: float64 In [244]: s.rename(str.upper) Out[244]: 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 [245]: df.rename(columns={'one': 'foo', 'two': 'bar'}, .....: index={'a': 'apple', 'b': 'banana', 'd': 'durian'}) .....: Out[245]: 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.
DataFrame.rename() also supports an “axis-style” calling convention, where you specify a single mapper and the axis to apply that mapping to.
DataFrame.rename()
mapper
In [246]: df.rename({'one': 'foo', 'two': 'bar'}, axis='columns') Out[246]: 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 [247]: df.rename({'a': 'apple', 'b': 'banana', 'd': 'durian'}, axis='index') Out[247]: 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.
inplace
False
inplace=True
Finally, rename() also accepts a scalar or list-like for altering the Series.name attribute.
Series.name
In [248]: s.rename("scalar-name") Out[248]: 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 DataFrame.rename_axis() and Series.rename_axis() allow specific names of a MultiIndex to be changed (as opposed to the labels).
DataFrame.rename_axis()
Series.rename_axis()
In [249]: 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 [250]: df Out[250]: 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 [251]: df.rename_axis(index={'let': 'abc'}) Out[251]: 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 [252]: df.rename_axis(index=str.upper) Out[252]: 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
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:
for i in object
Series: values
DataFrame: column labels
Thus, for example, iterating over a DataFrame gives you the column names:
In [253]: df = pd.DataFrame({'col1': np.random.randn(3), .....: 'col2': np.random.randn(3)}, index=['a', 'b', 'c']) .....: In [254]: for col in df: .....: print(col) .....: col1 col2
Pandas objects also have the dict-like items() method to iterate over the (key, value) pairs.
items()
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.
iterrows()
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.
itertuples()
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.
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 [255]: df = pd.DataFrame({'a': [1, 2, 3], 'b': ['a', 'b', 'c']}) In [256]: for index, row in df.iterrows(): .....: row['a'] = 10 .....: In [257]: df Out[257]: a b 0 1 a 1 2 b 2 3 c
Consistent with the dict-like interface, items() iterates through key-value pairs:
Series: (index, scalar value) pairs
DataFrame: (column, Series) pairs
In [258]: 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() 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 [259]: 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
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 [260]: df_orig = pd.DataFrame([[1, 1.5]], columns=['int', 'float']) In [261]: df_orig.dtypes Out[261]: int int64 float float64 dtype: object In [262]: row = next(df_orig.iterrows())[1] In [263]: row Out[263]: 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:
row
In [264]: row['int'].dtype Out[264]: dtype('float64') In [265]: df_orig['int'].dtype Out[265]: 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 [266]: df2 = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]}) In [267]: print(df2) x y 0 1 4 1 2 5 2 3 6 In [268]: print(df2.T) 0 1 2 x 1 2 3 y 4 5 6 In [269]: df2_t = pd.DataFrame({idx: values for idx, values in df2.iterrows()}) In [270]: print(df2_t) 0 1 2 x 1 2 3 y 4 5 6
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 [271]: 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().
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.
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 [272]: s = pd.Series(pd.date_range('20130101 09:10:12', periods=4)) In [273]: s Out[273]: 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 [274]: s.dt.hour Out[274]: 0 9 1 9 2 9 3 9 dtype: int64 In [275]: s.dt.second Out[275]: 0 12 1 12 2 12 3 12 dtype: int64 In [276]: s.dt.day Out[276]: 0 1 1 2 2 3 3 4 dtype: int64
This enables nice expressions like this:
In [277]: s[s.dt.day == 2] Out[277]: 1 2013-01-02 09:10:12 dtype: datetime64[ns]
You can easily produces tz aware transformations:
In [278]: stz = s.dt.tz_localize('US/Eastern') In [279]: stz Out[279]: 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 [280]: stz.dt.tz Out[280]: <DstTzInfo 'US/Eastern' LMT-1 day, 19:04:00 STD>
You can also chain these types of operations:
In [281]: s.dt.tz_localize('UTC').dt.tz_convert('US/Eastern') Out[281]: 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().
Series.dt.strftime()
strftime()
# DatetimeIndex In [282]: s = pd.Series(pd.date_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: datetime64[ns] 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
# PeriodIndex In [285]: s = pd.Series(pd.period_range('20130101', periods=4)) 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.strftime('%Y/%m/%d') Out[287]: 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.
.dt
# period In [288]: s = pd.Series(pd.period_range('20130101', periods=4, freq='D')) In [289]: s Out[289]: 0 2013-01-01 1 2013-01-02 2 2013-01-03 3 2013-01-04 dtype: period[D] In [290]: s.dt.year Out[290]: 0 2013 1 2013 2 2013 3 2013 dtype: int64 In [291]: s.dt.day Out[291]: 0 1 1 2 2 3 3 4 dtype: int64
# timedelta In [292]: s = pd.Series(pd.timedelta_range('1 day 00:00:05', periods=4, freq='s')) In [293]: s Out[293]: 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 [294]: s.dt.days Out[294]: 0 1 1 1 2 1 3 1 dtype: int64 In [295]: s.dt.seconds Out[295]: 0 5 1 6 2 7 3 8 dtype: int64 In [296]: s.dt.components Out[296]: 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
Series.dt will raise a TypeError if you access with a non-datetime-like values.
Series.dt
TypeError
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:
str
In [297]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'], .....: dtype="string") .....: In [298]: s.str.lower() Out[298]: 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).
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.
object
StringDtype
Please see Vectorized String Methods for a complete description.
Pandas supports three kinds of sorting: sorting by index labels, sorting by column values, and sorting by a combination of both.
The Series.sort_index() and DataFrame.sort_index() methods are used to sort a pandas object by its index levels.
Series.sort_index()
DataFrame.sort_index()
In [299]: 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 [300]: unsorted_df = df.reindex(index=['a', 'd', 'c', 'b'], .....: columns=['three', 'two', 'one']) .....: In [301]: unsorted_df Out[301]: 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 [302]: unsorted_df.sort_index() Out[302]: 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 [303]: unsorted_df.sort_index(ascending=False) Out[303]: 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 [304]: unsorted_df.sort_index(axis=1) Out[304]: 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 [305]: unsorted_df['three'].sort_index() Out[305]: a NaN b -0.098217 c 1.273388 d -0.252916 Name: three, dtype: float64
Sorting by index also supports a key parameter that takes a callable function to apply to the index being sorted. For MultiIndex objects, the key is applied per-level to the levels specified by level.
key
In [306]: s1 = pd.DataFrame({ .....: "a": ['B', 'a', 'C'], .....: "b": [1, 2, 3], .....: "c": [2, 3, 4] .....: }).set_index(list("ab")) .....: In [307]: s1 Out[307]: c a b B 1 2 a 2 3 C 3 4
In [308]: s1.sort_index(level="a") Out[308]: c a b B 1 2 C 3 4 a 2 3 In [309]: s1.sort_index(level="a", key=lambda idx: idx.str.lower()) Out[309]: c a b a 2 3 B 1 2 C 3 4
For information on key sorting by value, see value sorting.
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.
Series.sort_values()
DataFrame.sort_values()
by
In [310]: df1 = pd.DataFrame({'one': [2, 1, 1, 1], .....: 'two': [1, 3, 2, 4], .....: 'three': [5, 4, 3, 2]}) .....: In [311]: df1.sort_values(by='two') Out[311]: 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 [312]: df1[['one', 'two', 'three']].sort_values(by=['one', 'two']) Out[312]: 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:
na_position
In [313]: s[2] = np.nan In [314]: s.sort_values() Out[314]: 0 A 3 Aaba 1 B 4 Baca 6 CABA 8 cat 7 dog 2 <NA> 5 <NA> dtype: string In [315]: s.sort_values(na_position='first') Out[315]: 2 <NA> 5 <NA> 0 A 3 Aaba 1 B 4 Baca 6 CABA 8 cat 7 dog dtype: string
Sorting also supports a key parameter that takes a callable function to apply to the values being sorted.
In [316]: s1 = pd.Series(['B', 'a', 'C'])
In [317]: s1.sort_values() Out[317]: 0 B 2 C 1 a dtype: object In [318]: s1.sort_values(key=lambda x: x.str.lower()) Out[318]: 1 a 0 B 2 C dtype: object
key will be given the Series of values and should return a Series or array of the same shape with the transformed values. For DataFrame objects, the key is applied per column, so the key should still expect a Series and return a Series, e.g.
In [319]: df = pd.DataFrame({"a": ['B', 'a', 'C'], "b": [1, 2, 3]})
In [320]: df.sort_values(by='a') Out[320]: a b 0 B 1 2 C 3 1 a 2 In [321]: df.sort_values(by='a', key=lambda col: col.str.lower()) Out[321]: a b 1 a 2 0 B 1 2 C 3
The name or type of each column can be used to apply different functions to different columns.
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 [322]: idx = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('a', 2), .....: ('b', 2), ('b', 1), ('b', 1)]) .....: In [323]: idx.names = ['first', 'second'] # Build DataFrame In [324]: df_multi = pd.DataFrame({'A': np.arange(6, 0, -1)}, .....: index=idx) .....: In [325]: df_multi Out[325]: 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 [326]: df_multi.sort_values(by=['second', 'A']) Out[326]: A first second b 1 1 1 2 a 1 6 b 2 3 a 2 4 2 5
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.
Series has the searchsorted() method, which works similarly to numpy.ndarray.searchsorted().
searchsorted()
numpy.ndarray.searchsorted()
In [327]: ser = pd.Series([1, 2, 3]) In [328]: ser.searchsorted([0, 3]) Out[328]: array([0, 2]) In [329]: ser.searchsorted([0, 4]) Out[329]: array([0, 3]) In [330]: ser.searchsorted([1, 3], side='right') Out[330]: array([1, 3]) In [331]: ser.searchsorted([1, 3], side='left') Out[331]: array([0, 2]) In [332]: ser = pd.Series([3, 1, 2]) In [333]: ser.searchsorted([0, 3], sorter=np.argsort(ser)) Out[333]: array([0, 2])
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.
nsmallest()
nlargest()
head(n)
In [334]: s = pd.Series(np.random.permutation(10)) In [335]: s Out[335]: 0 2 1 0 2 3 3 7 4 1 5 5 6 9 7 6 8 8 9 4 dtype: int64 In [336]: s.sort_values() Out[336]: 1 0 4 1 0 2 2 3 9 4 5 5 7 6 3 7 8 8 6 9 dtype: int64 In [337]: s.nsmallest(3) Out[337]: 1 0 4 1 0 2 dtype: int64 In [338]: s.nlargest(3) Out[338]: 6 9 8 8 3 7 dtype: int64
DataFrame also has the nlargest and nsmallest methods.
nlargest
nsmallest
In [339]: 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 [340]: df.nlargest(3, 'a') Out[340]: a b c 5 11 f 3.0 3 10 c 3.2 4 8 e NaN In [341]: df.nlargest(5, ['a', 'c']) Out[341]: 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 [342]: df.nsmallest(3, 'a') Out[342]: a b c 0 -2 a 1.0 1 -1 b 2.0 6 -1 f 4.0 In [343]: df.nsmallest(5, ['a', 'c']) Out[343]: 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
You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to by.
In [344]: df1.columns = pd.MultiIndex.from_tuples([('a', 'one'), .....: ('a', 'two'), .....: ('b', 'three')]) .....: In [345]: df1.sort_values(by=('a', 'two')) Out[345]: a b one two three 0 2 1 5 2 1 2 3 1 1 3 4 3 1 4 2
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:
copy()
Inserting, deleting, or modifying a column.
Assigning to the index or columns attributes.
index
columns
For homogeneous data, directly modifying the values via the values attribute or advanced indexing.
values
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.
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).
float
int
timedelta64[ns]
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
arrays.DatetimeArray
'datetime64[ns, <tz>]'
Time zone handling
Categorical
CategoricalDtype
(none)
'category'
Categorical data
period (time spans)
PeriodDtype
Period
arrays.PeriodArray
'period[<freq>]', 'Period[<freq>]'
'period[<freq>]'
'Period[<freq>]'
Time span representation
sparse
SparseDtype
arrays.SparseArray
'Sparse', 'Sparse[int]', 'Sparse[float]'
'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>]]'
'interval'
'Interval'
'Interval[<numpy_dtype>]'
'Interval[datetime64[ns, <tz>]]'
'Interval[timedelta64[<freq>]]'
IntervalIndex
nullable integer
Int64Dtype, …
Int64Dtype
arrays.IntegerArray
'Int8', 'Int16', 'Int32', 'Int64', 'UInt8', 'UInt16', 'UInt32', 'UInt64'
'Int8'
'Int16'
'Int32'
'Int64'
'UInt8'
'UInt16'
'UInt32'
'UInt64'
Nullable integer data type
Strings
arrays.StringArray
'string'
Working with text data
Boolean (with NA)
BooleanDtype
arrays.BooleanArray
'boolean'
Boolean data with missing values
Pandas has two ways to store strings.
object dtype, which can hold any Python object, including strings.
StringDtype, which is dedicated to strings.
Generally, we recommend using StringDtype. See Text data types for 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.
dtypes
In [346]: 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 [347]: dft Out[347]: 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 [348]: dft.dtypes Out[348]: 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 [349]: dft['A'].dtype Out[349]: 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 [350]: pd.Series([1, 2, 3, 4, 5, 6.]) Out[350]: 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 [351]: pd.Series([1, 2, 3, 6., 'foo']) Out[351]: 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().
DataFrame.dtypes.value_counts()
In [352]: dft.dtypes.value_counts() Out[352]: bool 1 object 1 datetime64[ns] 1 float64 1 float32 1 int8 1 int64 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.
ndarray
In [353]: df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32') In [354]: df1 Out[354]: 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 [355]: df1.dtypes Out[355]: A float32 dtype: object In [356]: 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 [357]: df2 Out[357]: 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 [358]: df2.dtypes Out[358]: A float16 B float64 C uint8 dtype: object
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.
int64
float64
In [359]: pd.DataFrame([1, 2], columns=['a']).dtypes Out[359]: a int64 dtype: object In [360]: pd.DataFrame({'a': [1, 2]}).dtypes Out[360]: a int64 dtype: object In [361]: pd.DataFrame({'a': 1}, index=list(range(2))).dtypes Out[361]: 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.
int32
In [362]: frame = pd.DataFrame(np.array([1, 2]))
Types can potentially be upcasted when combined with other types, meaning they are promoted from the current type (e.g. int to float).
In [363]: df3 = df1.reindex_like(df2).fillna(value=0.0) + df2 In [364]: df3 Out[364]: 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 [365]: df3.dtypes Out[365]: 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 [366]: df3.to_numpy().dtype Out[366]: dtype('float64')
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.
astype()
copy=False
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 [367]: df3 Out[367]: 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 [368]: df3.dtypes Out[368]: A float32 B float64 C float64 dtype: object # conversion of dtypes In [369]: df3.astype('float32').dtypes Out[369]: A float32 B float32 C float32 dtype: object
Convert a subset of columns to a specified type using astype().
In [370]: dft = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]}) In [371]: dft[['a', 'b']] = dft[['a', 'b']].astype(np.uint8) In [372]: dft Out[372]: a b c 0 1 4 7 1 2 5 8 2 3 6 9 In [373]: dft.dtypes Out[373]: a uint8 b uint8 c int64 dtype: object
Convert certain columns to a specific dtype by passing a dict to astype().
In [374]: dft1 = pd.DataFrame({'a': [1, 0, 1], 'b': [4, 5, 6], 'c': [7, 8, 9]}) In [375]: dft1 = dft1.astype({'a': np.bool, 'c': np.float64}) In [376]: dft1 Out[376]: a b c 0 True 4 7.0 1 False 5 8.0 2 True 6 9.0 In [377]: dft1.dtypes Out[377]: a bool b int64 c float64 dtype: object
When trying to convert a subset of columns to a specified type using astype() and loc(), upcasting occurs.
loc()
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 [378]: dft = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]}) In [379]: dft.loc[:, ['a', 'b']].astype(np.uint8).dtypes Out[379]: a uint8 b uint8 dtype: object In [380]: dft.loc[:, ['a', 'b']] = dft.loc[:, ['a', 'b']].astype(np.uint8) In [381]: dft.dtypes Out[381]: a int64 b int64 c int64 dtype: object
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.
DataFrame.infer_objects()
Series.infer_objects()
In [382]: import datetime In [383]: df = pd.DataFrame([[1, 2], .....: ['a', 'b'], .....: [datetime.datetime(2016, 3, 2), .....: datetime.datetime(2016, 3, 2)]]) .....: In [384]: df = df.T In [385]: df Out[385]: 0 1 2 0 1 a 2016-03-02 1 2 b 2016-03-02 In [386]: df.dtypes Out[386]: 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.
infer_objects
In [387]: df.infer_objects().dtypes Out[387]: 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)
to_numeric()
In [388]: m = ['1.1', 2, 3] In [389]: pd.to_numeric(m) Out[389]: array([1.1, 2. , 3. ])
to_datetime() (conversion to datetime objects)
to_datetime()
In [390]: import datetime In [391]: m = ['2016-07-09', datetime.datetime(2016, 3, 2)] In [392]: pd.to_datetime(m) Out[392]: DatetimeIndex(['2016-07-09', '2016-03-02'], dtype='datetime64[ns]', freq=None)
to_timedelta() (conversion to timedelta objects)
to_timedelta()
In [393]: m = ['5us', pd.Timedelta('1day')] In [394]: pd.to_timedelta(m) Out[394]: 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:
errors
errors='raise'
errors='coerce'
pd.NaT
np.nan
In [395]: import datetime In [396]: m = ['apple', datetime.datetime(2016, 3, 2)] In [397]: pd.to_datetime(m, errors='coerce') Out[397]: DatetimeIndex(['NaT', '2016-03-02'], dtype='datetime64[ns]', freq=None) In [398]: m = ['apple', 2, 3] In [399]: pd.to_numeric(m, errors='coerce') Out[399]: array([nan, 2., 3.]) In [400]: m = ['apple', pd.Timedelta('1day')] In [401]: pd.to_timedelta(m, errors='coerce') Out[401]: 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:
errors='ignore'
In [402]: import datetime In [403]: m = ['apple', datetime.datetime(2016, 3, 2)] In [404]: pd.to_datetime(m, errors='ignore') Out[404]: Index(['apple', 2016-03-02 00:00:00], dtype='object') In [405]: m = ['apple', 2, 3] In [406]: pd.to_numeric(m, errors='ignore') Out[406]: array(['apple', 2, 3], dtype=object) In [407]: m = ['apple', pd.Timedelta('1day')] In [408]: pd.to_timedelta(m, errors='ignore') Out[408]: 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:
downcast
In [409]: m = ['1', 2, 3] In [410]: pd.to_numeric(m, downcast='integer') # smallest signed int dtype Out[410]: array([1, 2, 3], dtype=int8) In [411]: pd.to_numeric(m, downcast='signed') # same as 'integer' Out[411]: array([1, 2, 3], dtype=int8) In [412]: pd.to_numeric(m, downcast='unsigned') # smallest unsigned int dtype Out[412]: array([1, 2, 3], dtype=uint8) In [413]: pd.to_numeric(m, downcast='float') # smallest float dtype Out[413]: 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 [414]: import datetime In [415]: df = pd.DataFrame([ .....: ['2016-07-09', datetime.datetime(2016, 3, 2)]] * 2, dtype='O') .....: In [416]: df Out[416]: 0 1 0 2016-07-09 2016-03-02 00:00:00 1 2016-07-09 2016-03-02 00:00:00 In [417]: df.apply(pd.to_datetime) Out[417]: 0 1 0 2016-07-09 2016-03-02 1 2016-07-09 2016-03-02 In [418]: df = pd.DataFrame([['1.1', 2, 3]] * 2, dtype='O') In [419]: df Out[419]: 0 1 2 0 1.1 2 3 1 1.1 2 3 In [420]: df.apply(pd.to_numeric) Out[420]: 0 1 2 0 1.1 2 3 1 1.1 2 3 In [421]: df = pd.DataFrame([['5us', pd.Timedelta('1day')]] * 2, dtype='O') In [422]: df Out[422]: 0 1 0 5us 1 days 00:00:00 1 5us 1 days 00:00:00 In [423]: df.apply(pd.to_timedelta) Out[423]: 0 1 0 0 days 00:00:00.000005 1 days 1 0 days 00:00:00.000005 1 days
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.
integer
floating
In [424]: dfi = df3.astype('int32') In [425]: dfi['E'] = 1 In [426]: dfi Out[426]: 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 [427]: dfi.dtypes Out[427]: A int32 B int32 C int32 E int64 dtype: object In [428]: casted = dfi[dfi > 0] In [429]: casted Out[429]: 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 [430]: casted.dtypes Out[430]: A float64 B float64 C float64 E int64 dtype: object
While float dtypes are unchanged.
In [431]: dfa = df3.copy() In [432]: dfa['A'] = dfa['A'].astype('float32') In [433]: dfa.dtypes Out[433]: A float32 B float64 C float64 dtype: object In [434]: casted = dfa[df2 > 0] In [435]: casted Out[435]: 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 [436]: casted.dtypes Out[436]: A float32 B float64 C float64 dtype: object
The select_dtypes() method implements subsetting of columns based on their dtype.
select_dtypes()
First, let’s create a DataFrame with a slew of different dtypes:
In [437]: 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 [438]: df['tdeltas'] = df.dates.diff() In [439]: df['uint64'] = np.arange(3, 6).astype('u8') In [440]: df['other_dates'] = pd.date_range('20130101', periods=3) In [441]: df['tz_aware_dates'] = pd.date_range('20130101', periods=3, tz='US/Eastern') In [442]: df Out[442]: string int64 uint8 float64 bool1 ... category tdeltas uint64 other_dates tz_aware_dates 0 a 1 3 4.0 True ... A NaT 3 2013-01-01 2013-01-01 00:00:00-05:00 1 b 2 4 5.0 False ... B 1 days 4 2013-01-02 2013-01-02 00:00:00-05:00 2 c 3 5 6.0 True ... C 1 days 5 2013-01-03 2013-01-03 00:00:00-05:00 [3 rows x 12 columns]
And the dtypes:
In [443]: df.dtypes Out[443]: 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 [444]: df.select_dtypes(include=[bool]) Out[444]: 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 [445]: df.select_dtypes(include=['bool']) Out[445]: 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 [446]: df.select_dtypes(include=['number', 'bool'], exclude=['unsignedinteger']) Out[446]: 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 [447]: df.select_dtypes(include=['object']) Out[447]: 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:
numpy.number
In [448]: 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:
numpy.generic
In [449]: subdtypes(np.generic) Out[449]: [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_]]
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.
category
datetime64[ns, tz]