Essential Basic Functionality

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

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

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

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

In [4]: wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'],
   ...:            major_axis=date_range('1/1/2000', periods=5),
   ...:            minor_axis=['A', 'B', 'C', 'D'])
   ...: 

Head and Tail

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

In [5]: long_series = Series(randn(1000))

In [6]: long_series.head()
Out[6]: 
0   -0.199038
1    1.095864
2   -0.200875
3    0.162291
4   -0.430489
dtype: float64

In [7]: long_series.tail(3)
Out[7]: 
997   -1.198693
998    1.238029
999   -1.344716
dtype: float64

Attributes and the raw ndarray(s)

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

• shape: gives the axis dimensions of the object, consistent with ndarray
• Axis labels
  • Series: index (only axis)
  • DataFrame: index (rows) and columns
  • Panel: items, major_axis, and minor_axis

Note, these attributes can be safely assigned to!

In [8]: df[:2]
Out[8]: 
                   A         B         C
2000-01-01  0.232465 -0.789552 -0.364308
2000-01-02 -0.534541  0.822239 -0.443109

[2 rows x 3 columns]

In [9]: df.columns = [x.lower() for x in df.columns]

In [10]: df
Out[10]: 
                   a         b         c
2000-01-01  0.232465 -0.789552 -0.364308
2000-01-02 -0.534541  0.822239 -0.443109
2000-01-03 -2.119990 -0.460149  1.813962
2000-01-04 -1.053571  0.009412 -0.165966
2000-01-05 -0.848662 -0.495553 -0.176421
2000-01-06 -0.423595 -1.035433 -1.035374
2000-01-07 -2.369079  0.524408 -0.871120
2000-01-08  1.585433  0.039501  2.274101

[8 rows x 3 columns]

To get the actual data inside a data structure, one need only access the values property:

In [11]: s.values
Out[11]: array([ 1.1292,  0.2313, -0.1847, -0.1386, -0.9243])

In [12]: df.values
Out[12]: 
array([[ 0.2325, -0.7896, -0.3643],
       [-0.5345,  0.8222, -0.4431],
       [-2.12  , -0.4601,  1.814 ],
       [-1.0536,  0.0094, -0.166 ],
       [-0.8487, -0.4956, -0.1764],
       [-0.4236, -1.0354, -1.0354],
       [-2.3691,  0.5244, -0.8711],
       [ 1.5854,  0.0395,  2.2741]])

In [13]: wp.values
Out[13]: 
array([[[-1.1181,  0.4313,  0.5547, -1.3336],
        [-0.3322, -0.4859,  1.7259,  1.7993],
        [-0.9689, -0.7795, -2.0007, -1.8666],
        [-1.1013,  1.9575,  0.0589,  0.7581],
        [ 0.0766, -0.5485, -0.1605, -0.3778]],

       [[ 0.2499, -0.3413, -0.2726, -0.2774],
        [-1.1029,  0.1003, -1.6028,  0.9201],
        [-0.6439,  0.0603, -0.4349, -0.4943],
        [ 0.738 ,  0.4516,  0.3341, -0.7871],
        [ 0.6514, -0.7419,  1.1939, -2.3958]]])

If a DataFrame or Panel 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.

Accelerated operations

Pandas has support for accelerating certain types of binary numerical and boolean operations using the numexpr library (starting in 0.11.0) and the bottleneck libraries.

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

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

Operation	0.11.0 (ms)	Prior Version (ms)	Ratio to Prior
df1 > df2	13.32	125.35	0.1063
df1 * df2	21.71	36.63	0.5928
df1 + df2	22.04	36.50	0.6039

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

Flexible binary operations

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

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

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

Matching / broadcasting behavior

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

In [14]: d = {'one' : Series(randn(3), index=['a', 'b', 'c']),
   ....:      'two' : Series(randn(4), index=['a', 'b', 'c', 'd']),
   ....:      'three' : Series(randn(3), index=['b', 'c', 'd'])}
   ....: 

In [15]: df = df_orig = DataFrame(d)

In [16]: df
Out[16]: 
        one     three       two
a -0.701368       NaN -0.087103
b  0.109333 -0.354359  0.637674
c -0.231617 -0.148387 -0.002666
d       NaN -0.167407  0.104044

[4 rows x 3 columns]

In [17]: row = df.ix[1]

In [18]: column = df['two']

In [19]: df.sub(row, axis='columns')
Out[19]: 
        one     three       two
a -0.810701       NaN -0.724777
b  0.000000  0.000000  0.000000
c -0.340950  0.205973 -0.640340
d       NaN  0.186952 -0.533630

[4 rows x 3 columns]

In [20]: df.sub(row, axis=1)
Out[20]: 
        one     three       two
a -0.810701       NaN -0.724777
b  0.000000  0.000000  0.000000
c -0.340950  0.205973 -0.640340
d       NaN  0.186952 -0.533630

[4 rows x 3 columns]

In [21]: df.sub(column, axis='index')
Out[21]: 
        one     three  two
a -0.614265       NaN    0
b -0.528341 -0.992033    0
c -0.228950 -0.145720    0
d       NaN -0.271451    0

[4 rows x 3 columns]

In [22]: df.sub(column, axis=0)
Out[22]: 
        one     three  two
a -0.614265       NaN    0
b -0.528341 -0.992033    0
c -0.228950 -0.145720    0
d       NaN -0.271451    0

[4 rows x 3 columns]

With Panel, describing the matching behavior is a bit more difficult, so the arithmetic methods instead (and perhaps confusingly?) give you the option to specify the broadcast axis. For example, suppose we wished to demean the data over a particular axis. This can be accomplished by taking the mean over an axis and broadcasting over the same axis:

In [23]: major_mean = wp.mean(axis='major')

In [24]: major_mean
Out[24]: 
      Item1     Item2
A -0.688773 -0.021497
B  0.114982 -0.094183
C  0.035674 -0.156470
D -0.204142 -0.606887

[4 rows x 2 columns]

In [25]: wp.sub(major_mean, axis='major')
Out[25]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to D

And similarly for axis="items" and axis="minor".

Note

I could be convinced to make the axis argument in the DataFrame methods match the broadcasting behavior of Panel. Though it would require a transition period so users can change their code...

Missing data / operations with fill values

In Series and DataFrame (though not yet in Panel), the arithmetic functions have the option of inputting a fill_value, namely a value to substitute when at most one of the values at a location are missing. For example, when adding two DataFrame objects, you may wish to treat NaN as 0 unless both DataFrames are missing that value, in which case the result will be NaN (you can later replace NaN with some other value using fillna if you wish).

In [26]: df
Out[26]: 
        one     three       two
a -0.701368       NaN -0.087103
b  0.109333 -0.354359  0.637674
c -0.231617 -0.148387 -0.002666
d       NaN -0.167407  0.104044

[4 rows x 3 columns]

In [27]: df2
Out[27]: 
        one     three       two
a -0.701368  1.000000 -0.087103
b  0.109333 -0.354359  0.637674
c -0.231617 -0.148387 -0.002666
d       NaN -0.167407  0.104044

[4 rows x 3 columns]

In [28]: df + df2
Out[28]: 
        one     three       two
a -1.402736       NaN -0.174206
b  0.218666 -0.708719  1.275347
c -0.463233 -0.296773 -0.005333
d       NaN -0.334814  0.208088

[4 rows x 3 columns]

In [29]: df.add(df2, fill_value=0)
Out[29]: 
        one     three       two
a -1.402736  1.000000 -0.174206
b  0.218666 -0.708719  1.275347
c -0.463233 -0.296773 -0.005333
d       NaN -0.334814  0.208088

[4 rows x 3 columns]

Flexible Comparisons

Starting in v0.8, pandas introduced binary comparison methods eq, ne, lt, gt, le, and ge to Series and DataFrame whose behavior is analogous to the binary arithmetic operations described above:

In [30]: df.gt(df2)
Out[30]: 
     one  three    two
a  False  False  False
b  False  False  False
c  False  False  False
d  False  False  False

[4 rows x 3 columns]

In [31]: df2.ne(df)
Out[31]: 
     one  three    two
a  False   True  False
b  False  False  False
c  False  False  False
d   True  False  False

[4 rows x 3 columns]

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

Boolean Reductions

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

In [32]: (df>0).all()
Out[32]: 
one      False
three    False
two      False
dtype: bool

In [33]: (df>0).any()
Out[33]: 
one       True
three    False
two       True
dtype: bool

You can reduce to a final boolean value.

In [34]: (df>0).any().any()
Out[34]: True

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

In [35]: df.empty
Out[35]: False

In [36]: DataFrame(columns=list('ABC')).empty
Out[36]: True

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

In [37]: Series([True]).bool()
Out[37]: True

In [38]: Series([False]).bool()
Out[38]: False

In [39]: DataFrame([[True]]).bool()
Out[39]: True

In [40]: DataFrame([[False]]).bool()
Out[40]: False

Warning

You might be tempted to do the following:

>>>if df:
     ...

Or

>>> df and df2

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

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

See gotchas for a more detailed discussion.

Comparing if objects are equivalent

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

In [41]: df+df == df*2
Out[41]: 
     one  three   two
a   True  False  True
b   True   True  True
c   True   True  True
d  False   True  True

[4 rows x 3 columns]

In [42]: (df+df == df*2).all()
Out[42]: 
one      False
three    False
two       True
dtype: bool

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

In [43]: np.nan == np.nan
Out[43]: False

So, as of v0.13.1, NDFrames (such as Series, DataFrames, and Panels) have an equals method for testing equality, with NaNs in corresponding locations treated as equal.

In [44]: (df+df).equals(df*2)
Out[44]: True

Combining overlapping data sets

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

In [45]: df1 = DataFrame({'A' : [1., np.nan, 3., 5., np.nan],
   ....:                  'B' : [np.nan, 2., 3., np.nan, 6.]})
   ....: 

In [46]: df2 = DataFrame({'A' : [5., 2., 4., np.nan, 3., 7.],
   ....:                  'B' : [np.nan, np.nan, 3., 4., 6., 8.]})
   ....: 

In [47]: df1
Out[47]: 
    A   B
0   1 NaN
1 NaN   2
2   3   3
3   5 NaN
4 NaN   6

[5 rows x 2 columns]

In [48]: df2
Out[48]: 
    A   B
0   5 NaN
1   2 NaN
2   4   3
3 NaN   4
4   3   6
5   7   8

[6 rows x 2 columns]

In [49]: df1.combine_first(df2)
Out[49]: 
   A   B
0  1 NaN
1  2   2
2  3   3
3  5   4
4  3   6
5  7   8

[6 rows x 2 columns]

General DataFrame Combine

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

So, for instance, to reproduce combine_first as above:

In [50]: combiner = lambda x, y: np.where(isnull(x), y, x)

In [51]: df1.combine(df2, combiner)
Out[51]: 
   A   B
0  1 NaN
1  2   2
2  3   3
3  5   4
4  3   6
5  7   8

[6 rows x 2 columns]

Descriptive statistics

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

• Series: no axis argument needed
• DataFrame: “index” (axis=0, default), “columns” (axis=1)
• Panel: “items” (axis=0), “major” (axis=1, default), “minor” (axis=2)

For example:

In [52]: df
Out[52]: 
        one     three       two
a -0.701368       NaN -0.087103
b  0.109333 -0.354359  0.637674
c -0.231617 -0.148387 -0.002666
d       NaN -0.167407  0.104044

[4 rows x 3 columns]

In [53]: df.mean(0)
Out[53]: 
one     -0.274551
three   -0.223384
two      0.162987
dtype: float64

In [54]: df.mean(1)
Out[54]: 
a   -0.394235
b    0.130882
c   -0.127557
d   -0.031682
dtype: float64

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

In [55]: df.sum(0, skipna=False)
Out[55]: 
one           NaN
three         NaN
two      0.651948
dtype: float64

In [56]: df.sum(axis=1, skipna=True)
Out[56]: 
a   -0.788471
b    0.392647
c   -0.382670
d   -0.063363
dtype: float64

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

In [57]: ts_stand = (df - df.mean()) / df.std()

In [58]: ts_stand.std()
Out[58]: 
one      1
three    1
two      1
dtype: float64

In [59]: xs_stand = df.sub(df.mean(1), axis=0).div(df.std(1), axis=0)

In [60]: xs_stand.std(1)
Out[60]: 
a    1
b    1
c    1
d    1
dtype: float64

Note that methods like cumsum and cumprod preserve the location of NA values:

In [61]: df.cumsum()
Out[61]: 
        one     three       two
a -0.701368       NaN -0.087103
b -0.592035 -0.354359  0.550570
c -0.823652 -0.502746  0.547904
d       NaN -0.670153  0.651948

[4 rows x 3 columns]

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

Function	Description
count	Number of non-null 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	Unbiased standard deviation
var	Unbiased variance
skew	Unbiased skewness (3rd moment)
kurt	Unbiased 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 [62]: np.mean(df['one'])
Out[62]: -0.27455055654271204

In [63]: np.mean(df['one'].values)
Out[63]: nan

Series also has a method nunique which will return the number of unique non-null values:

In [64]: series = Series(randn(500))

In [65]: series[20:500] = np.nan

In [66]: series[10:20]  = 5

In [67]: series.nunique()
Out[67]: 11

Summarizing data: describe

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

In [68]: series = Series(randn(1000))

In [69]: series[::2] = np.nan

In [70]: series.describe()
Out[70]: 
count    500.000000
mean      -0.019898
std        1.019180
min       -2.628792
25%       -0.649795
50%       -0.059405
75%        0.651932
max        3.240991
dtype: float64

In [71]: frame = DataFrame(randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])

In [72]: frame.ix[::2] = np.nan

In [73]: frame.describe()
Out[73]: 
                a           b           c           d           e
count  500.000000  500.000000  500.000000  500.000000  500.000000
mean     0.051388    0.053476   -0.035612    0.015388    0.057804
std      0.989217    0.995961    0.977047    0.968385    1.022528
min     -3.224136   -2.606460   -2.762875   -2.961757   -2.829100
25%     -0.657420   -0.597123   -0.688961   -0.695019   -0.738097
50%      0.042928    0.018837   -0.071830   -0.011326    0.073287
75%      0.702445    0.693542    0.600454    0.680924    0.807670
max      3.034008    3.104512    2.812028    2.623914    3.542846

[8 rows x 5 columns]

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

In [74]: s = Series(['a', 'a', 'b', 'b', 'a', 'a', np.nan, 'c', 'd', 'a'])

In [75]: s.describe()
Out[75]: 
count     9
unique    4
top       a
freq      5
dtype: object

There also is a utility function, value_range which takes a DataFrame and returns a series with the minimum/maximum values in the DataFrame.

Index of Min/Max Values

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

In [76]: s1 = Series(randn(5))

In [77]: s1
Out[77]: 
0   -0.574018
1    0.668292
2    0.303418
3   -1.190271
4    0.138399
dtype: float64

In [78]: s1.idxmin(), s1.idxmax()
Out[78]: (3, 1)

In [79]: df1 = DataFrame(randn(5,3), columns=['A','B','C'])

In [80]: df1
Out[80]: 
          A         B         C
0 -0.184355 -1.054354 -1.613138
1 -0.050807 -2.130168 -1.852271
2  0.455674  2.571061 -1.152538
3 -1.638940 -0.364831 -0.348520
4  0.202856  0.777088 -0.358316

[5 rows x 3 columns]

In [81]: df1.idxmin(axis=0)
Out[81]: 
A    3
B    1
C    1
dtype: int64

In [82]: df1.idxmax(axis=1)
Out[82]: 
0    A
1    A
2    B
3    C
4    B
dtype: object

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

In [83]: df3 = DataFrame([2, 1, 1, 3, np.nan], columns=['A'], index=list('edcba'))

In [84]: df3
Out[84]: 
    A
e   2
d   1
c   1
b   3
a NaN

[5 rows x 1 columns]

In [85]: df3['A'].idxmin()
Out[85]: 'd'

Note

idxmin and idxmax are called argmin and argmax in NumPy.

Value counts (histogramming) / Mode

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

In [86]: data = np.random.randint(0, 7, size=50)

In [87]: data
Out[87]: 
array([4, 6, 6, 1, 2, 1, 0, 5, 3, 2, 4, 3, 1, 3, 5, 3, 0, 0, 4, 4, 6, 1, 0,
       4, 3, 2, 1, 3, 1, 5, 6, 3, 1, 2, 4, 4, 3, 3, 2, 2, 2, 3, 2, 3, 0, 1,
       2, 4, 5, 5])

In [88]: s = Series(data)

In [89]: s.value_counts()
Out[89]: 
3    11
2     9
4     8
1     8
5     5
0     5
6     4
dtype: int64

In [90]: value_counts(data)
Out[90]: 
3    11
2     9
4     8
1     8
5     5
0     5
6     4
dtype: int64

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

In [91]: s5 = Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])

In [92]: s5.mode()
Out[92]: 
0    3
1    7
dtype: int64

In [93]: df5 = DataFrame({"A": np.random.randint(0, 7, size=50),
   ....:                  "B": np.random.randint(-10, 15, size=50)})
   ....: 

In [94]: df5.mode()
Out[94]: 
   A   B
0  5  -4
1  6 NaN

[2 rows x 2 columns]

Discretization and quantiling

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

In [95]: arr = np.random.randn(20)

In [96]: factor = cut(arr, 4)

In [97]: factor
Out[97]: 
 (-0.886, -0.0912]
 (-0.886, -0.0912]
 (-0.886, -0.0912]
    (1.493, 2.285]
    (0.701, 1.493]
...
  (-0.0912, 0.701]
 (-0.886, -0.0912]
    (0.701, 1.493]
    (0.701, 1.493]
  (-0.0912, 0.701]
    (1.493, 2.285]
Levels (4): Index(['(-0.886, -0.0912]', '(-0.0912, 0.701]',
                   '(0.701, 1.493]', '(1.493, 2.285]'], dtype=object)
Length: 20

In [98]: factor = cut(arr, [-5, -1, 0, 1, 5])

In [99]: factor
Out[99]: 
 (-1, 0]
 (-1, 0]
 (-1, 0]
  (1, 5]
  (1, 5]
...
  (0, 1]
 (-1, 0]
  (0, 1]
  (0, 1]
  (0, 1]
  (1, 5]
Levels (4): Index(['(-5, -1]', '(-1, 0]', '(0, 1]', '(1, 5]'], dtype=object)
Length: 20

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

In [100]: arr = np.random.randn(30)

In [101]: factor = qcut(arr, [0, .25, .5, .75, 1])

In [102]: factor
Out[102]: 
 [-1.861, -0.487]
  (0.0554, 0.658]
   (0.658, 2.259]
 [-1.861, -0.487]
   (0.658, 2.259]
...
  (0.0554, 0.658]
  (0.0554, 0.658]
   (0.658, 2.259]
 [-1.861, -0.487]
  (0.0554, 0.658]
 (-0.487, 0.0554]
Levels (4): Index(['[-1.861, -0.487]', '(-0.487, 0.0554]',
                   '(0.0554, 0.658]', '(0.658, 2.259]'], dtype=object)
Length: 30

In [103]: value_counts(factor)
Out[103]: 
(0.658, 2.259]      8
[-1.861, -0.487]    8
(0.0554, 0.658]     7
(-0.487, 0.0554]    7
dtype: int64

We can also pass infinite values to define the bins:

In [104]: arr = np.random.randn(20)

In [105]: factor = cut(arr, [-np.inf, 0, np.inf])

In [106]: factor
Out[106]: 
  (0, inf]
  (0, inf]
 (-inf, 0]
  (0, inf]
 (-inf, 0]
...
 (-inf, 0]
  (0, inf]
  (0, inf]
 (-inf, 0]
  (0, inf]
 (-inf, 0]
Levels (2): Index(['(-inf, 0]', '(0, inf]'], dtype=object)
Length: 20

Function application

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

In [107]: df.apply(np.mean)
Out[107]: 
one     -0.274551
three   -0.223384
two      0.162987
dtype: float64

In [108]: df.apply(np.mean, axis=1)
Out[108]: 
a   -0.394235
b    0.130882
c   -0.127557
d   -0.031682
dtype: float64

In [109]: df.apply(lambda x: x.max() - x.min())
Out[109]: 
one      0.810701
three    0.205973
two      0.724777
dtype: float64

In [110]: df.apply(np.cumsum)
Out[110]: 
        one     three       two
a -0.701368       NaN -0.087103
b -0.592035 -0.354359  0.550570
c -0.823652 -0.502746  0.547904
d       NaN -0.670153  0.651948

[4 rows x 3 columns]

In [111]: df.apply(np.exp)
Out[111]: 
        one     three       two
a  0.495907       NaN  0.916583
b  1.115534  0.701623  1.892074
c  0.793250  0.862098  0.997337
d       NaN  0.845855  1.109649

[4 rows x 3 columns]

Depending on the return type of the function passed to apply, the result will either be of lower dimension or the same dimension.

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

In [113]: tsdf.apply(lambda x: x.idxmax())
Out[113]: 
A   2002-08-19
B   2000-11-30
C   2002-01-10
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 [114]: tsdf
Out[114]: 
                   A         B         C
2000-01-01 -1.226159  0.173875 -0.798063
2000-01-02  0.127076  0.141070 -2.186743
2000-01-03 -1.804229  0.879800  0.465165
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  1.542261  0.524780  1.445690
2000-01-09 -1.104998 -0.470200  0.336180
2000-01-10 -0.947692 -0.262122 -0.423769

[10 rows x 3 columns]

In [115]: tsdf.apply(Series.interpolate)
Out[115]: 
                   A         B         C
2000-01-01 -1.226159  0.173875 -0.798063
2000-01-02  0.127076  0.141070 -2.186743
2000-01-03 -1.804229  0.879800  0.465165
2000-01-04 -1.134931  0.808796  0.661270
2000-01-05 -0.465633  0.737792  0.857375
2000-01-06  0.203665  0.666788  1.053480
2000-01-07  0.872963  0.595784  1.249585
2000-01-08  1.542261  0.524780  1.445690
2000-01-09 -1.104998 -0.470200  0.336180
2000-01-10 -0.947692 -0.262122 -0.423769

[10 rows x 3 columns]

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.

See also

The section on GroupBy demonstrates related, flexible functionality for grouping by some criterion, applying, and combining the results into a Series, DataFrame, etc.

Applying elementwise Python functions

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

In [116]: df4
Out[116]: 
        one     three       two
a -0.701368       NaN -0.087103
b  0.109333 -0.354359  0.637674
c -0.231617 -0.148387 -0.002666
d       NaN -0.167407  0.104044

[4 rows x 3 columns]

In [117]: f = lambda x: len(str(x))

In [118]: df4['one'].map(f)
Out[118]: 
a    15
b    14
c    15
d     3
Name: one, dtype: int64

In [119]: df4.applymap(f)
Out[119]: 
   one  three  two
a   15      3   16
b   14     15   14
c   15     15   17
d    3     15   14

[4 rows x 3 columns]

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

In [120]: s = Series(['six', 'seven', 'six', 'seven', 'six'],
   .....:            index=['a', 'b', 'c', 'd', 'e'])
   .....: 

In [121]: t = Series({'six' : 6., 'seven' : 7.})

In [122]: s
Out[122]: 
a      six
b    seven
c      six
d    seven
e      six
dtype: object

In [123]: s.map(t)
Out[123]: 
a    6
b    7
c    6
d    7
e    6
dtype: float64

Applying with a Panel

Applying with a Panel will pass a Series to the applied function. If the applied function returns a Series, the result of the application will be a Panel. If the applied function reduces to a scalar, the result of the application will be a DataFrame.

Note

Prior to 0.13.1 apply on a Panel would only work on ufuncs (e.g. np.sum/np.max).

In [124]: import pandas.util.testing as tm

In [125]: panel = tm.makePanel(5)

In [126]: panel
Out[126]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D

In [127]: panel['ItemA']
Out[127]: 
                   A         B         C         D
2000-01-03  0.166882 -0.597361 -1.200639  0.174260
2000-01-04 -1.759496 -1.514940 -1.872993 -0.581163
2000-01-05  0.901336 -1.640398  0.825210  0.087916
2000-01-06 -0.317478 -1.130643 -0.392715  0.416971
2000-01-07 -0.681335 -0.245890 -1.994150  0.666084

[5 rows x 4 columns]

A transformational apply.

In [128]: result = panel.apply(lambda x: x*2, axis='items')

In [129]: result
Out[129]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D

In [130]: result['ItemA']
Out[130]: 
                   A         B         C         D
2000-01-03  0.333764 -1.194722 -2.401278  0.348520
2000-01-04 -3.518991 -3.029880 -3.745986 -1.162326
2000-01-05  1.802673 -3.280796  1.650421  0.175832
2000-01-06 -0.634955 -2.261286 -0.785430  0.833943
2000-01-07 -1.362670 -0.491779 -3.988300  1.332168

[5 rows x 4 columns]

A reduction operation.

In [131]: panel.apply(lambda x: x.dtype, axis='items')
Out[131]: 
                  A        B        C        D
2000-01-03  float64  float64  float64  float64
2000-01-04  float64  float64  float64  float64
2000-01-05  float64  float64  float64  float64
2000-01-06  float64  float64  float64  float64
2000-01-07  float64  float64  float64  float64

[5 rows x 4 columns]

A similar reduction type operation

In [132]: panel.apply(lambda x: x.sum(), axis='major_axis')
Out[132]: 
      ItemA     ItemB     ItemC
A -1.690090  1.840259  0.010754
B -5.129232  0.860182  0.178018
C -4.635286  0.545328  2.456520
D  0.764068 -3.623586  1.761541

[4 rows x 3 columns]

This last reduction is equivalent to

In [133]: panel.sum('major_axis')
Out[133]: 
      ItemA     ItemB     ItemC
A -1.690090  1.840259  0.010754
B -5.129232  0.860182  0.178018
C -4.635286  0.545328  2.456520
D  0.764068 -3.623586  1.761541

[4 rows x 3 columns]

A transformation operation that returns a Panel, but is computing the z-score across the major_axis.

In [134]: result = panel.apply(
   .....:            lambda x: (x-x.mean())/x.std(),
   .....:            axis='major_axis')
   .....: 

In [135]: result
Out[135]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D

In [136]: result['ItemA']
Out[136]: 
                   A         B         C         D
2000-01-03  0.509389  0.719204 -0.234072  0.045812
2000-01-04 -1.434116 -0.820934 -0.809328 -1.567858
2000-01-05  1.250373 -1.031513  1.499214 -0.138629
2000-01-06  0.020723 -0.175899  0.457175  0.564271
2000-01-07 -0.346370  1.309142 -0.912988  1.096405

[5 rows x 4 columns]

Apply can also accept multiple axes in the axis argument. This will pass a DataFrame of the cross-section to the applied function.

In [137]: f = lambda x: ((x.T-x.mean(1))/x.std(1)).T

In [138]: result = panel.apply(f, axis = ['items','major_axis'])

In [139]: result
Out[139]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
Items axis: A to D
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: ItemA to ItemC

In [140]: result.loc[:,:,'ItemA']
Out[140]: 
                   A         B         C         D
2000-01-03  0.783778 -0.648605 -0.903128  0.450190
2000-01-04 -0.884670 -1.046087 -1.096521 -0.900467
2000-01-05  1.140729 -1.124651  0.716895  0.754324
2000-01-06 -1.043494  0.029043 -0.991042  0.845339
2000-01-07 -1.125870 -0.536928 -1.152240 -0.182526

[5 rows x 4 columns]

This is equivalent to the following

In [141]: result = Panel(dict([ (ax,f(panel.loc[:,:,ax]))
   .....:                         for ax in panel.minor_axis ]))
   .....: 

In [142]: result
Out[142]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
Items axis: A to D
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: ItemA to ItemC

In [143]: result.loc[:,:,'ItemA']
Out[143]: 
                   A         B         C         D
2000-01-03  0.783778 -0.648605 -0.903128  0.450190
2000-01-04 -0.884670 -1.046087 -1.096521 -0.900467
2000-01-05  1.140729 -1.124651  0.716895  0.754324
2000-01-06 -1.043494  0.029043 -0.991042  0.845339
2000-01-07 -1.125870 -0.536928 -1.152240 -0.182526

[5 rows x 4 columns]

Reindexing and altering labels

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

• Reorders the existing data to match a new set of labels
• Inserts missing value (NA) markers in label locations where no data for that label existed
• If specified, fill data for missing labels using logic (highly relevant to working with time series data)

Here is a simple example:

In [144]: s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])

In [145]: s
Out[145]: 
a    1.112686
b   -1.069046
c   -1.218080
d   -0.944778
e    0.005240
dtype: float64

In [146]: s.reindex(['e', 'b', 'f', 'd'])
Out[146]: 
e    0.005240
b   -1.069046
f         NaN
d   -0.944778
dtype: float64

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

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

In [147]: df
Out[147]: 
        one     three       two
a -0.701368       NaN -0.087103
b  0.109333 -0.354359  0.637674
c -0.231617 -0.148387 -0.002666
d       NaN -0.167407  0.104044

[4 rows x 3 columns]

In [148]: df.reindex(index=['c', 'f', 'b'], columns=['three', 'two', 'one'])
Out[148]: 
      three       two       one
c -0.148387 -0.002666 -0.231617
f       NaN       NaN       NaN
b -0.354359  0.637674  0.109333

[3 rows x 3 columns]

For convenience, you may utilize the reindex_axis method, which takes the labels and a keyword axis parameter.

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

In [150]: rs
Out[150]: 
a    1.112686
b   -1.069046
c   -1.218080
d   -0.944778
dtype: float64

In [151]: rs.index is df.index
Out[151]: True

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

See also

Advanced indexing is an even more concise way of doing reindexing.

Note

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

Reindexing to align with another object

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

In [152]: df2
Out[152]: 
        one       two
a -0.701368 -0.087103
b  0.109333  0.637674
c -0.231617 -0.002666

[3 rows x 2 columns]

In [153]: df3
Out[153]: 
        one       two
a -0.426817 -0.269738
b  0.383883  0.455039
c  0.042934 -0.185301

[3 rows x 2 columns]

In [154]: df.reindex_like(df2)
Out[154]: 
        one       two
a -0.701368 -0.087103
b  0.109333  0.637674
c -0.231617 -0.002666

[3 rows x 2 columns]

Reindexing with reindex_axis

Aligning objects with each other with align

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

• join='outer': take the union of the indexes
• join='left': use the calling object’s index
• join='right': use the passed object’s index
• join='inner': intersect the indexes

It returns a tuple with both of the reindexed Series:

In [155]: s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])

In [156]: s1 = s[:4]

In [157]: s2 = s[1:]

In [158]: s1.align(s2)
Out[158]: 
(a    0.479090
 b    0.686579
 c   -0.949750
 d   -0.257472
 e         NaN
 dtype: float64, a         NaN
 b    0.686579
 c   -0.949750
 d   -0.257472
 e   -0.568459
 dtype: float64)

In [159]: s1.align(s2, join='inner')
Out[159]: 
(b    0.686579
 c   -0.949750
 d   -0.257472
 dtype: float64, b    0.686579
 c   -0.949750
 d   -0.257472
 dtype: float64)

In [160]: s1.align(s2, join='left')
Out[160]: 
(a    0.479090
 b    0.686579
 c   -0.949750
 d   -0.257472
 dtype: float64, a         NaN
 b    0.686579
 c   -0.949750
 d   -0.257472
 dtype: float64)

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

In [161]: df.align(df2, join='inner')
Out[161]: 
(        one       two
 a -0.701368 -0.087103
 b  0.109333  0.637674
 c -0.231617 -0.002666
 
 [3 rows x 2 columns],         one       two
 a -0.701368 -0.087103
 b  0.109333  0.637674
 c -0.231617 -0.002666
 
 [3 rows x 2 columns])

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

In [162]: df.align(df2, join='inner', axis=0)
Out[162]: 
(        one     three       two
 a -0.701368       NaN -0.087103
 b  0.109333 -0.354359  0.637674
 c -0.231617 -0.148387 -0.002666
 
 [3 rows x 3 columns],         one       two
 a -0.701368 -0.087103
 b  0.109333  0.637674
 c -0.231617 -0.002666
 
 [3 rows x 2 columns])

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

In [163]: df.align(df2.ix[0], axis=1)
Out[163]: 
(        one     three       two
 a -0.701368       NaN -0.087103
 b  0.109333 -0.354359  0.637674
 c -0.231617 -0.148387 -0.002666
 d       NaN -0.167407  0.104044
 
 [4 rows x 3 columns], one     -0.701368
 three         NaN
 two     -0.087103
 Name: a, dtype: float64)

Filling while reindexing

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

Method	Action
pad / ffill	Fill values forward
bfill / backfill	Fill values backward

Other fill methods could be added, of course, but these are the two most commonly used for time series data. In a way they only make sense for time series or otherwise ordered data, but you may have an application on non-time series data where this sort of “interpolation” logic is the correct thing to do. More sophisticated interpolation of missing values would be an obvious extension.

We illustrate these fill methods on a simple TimeSeries:

In [164]: rng = date_range('1/3/2000', periods=8)

In [165]: ts = Series(randn(8), index=rng)

In [166]: ts2 = ts[[0, 3, 6]]

In [167]: ts
Out[167]: 
2000-01-03   -0.059786
2000-01-04    0.936271
2000-01-05    0.040623
2000-01-06    0.836517
2000-01-07    1.849649
2000-01-08   -1.198994
2000-01-09    0.688500
2000-01-10   -0.696903
Freq: D, dtype: float64

In [168]: ts2
Out[168]: 
2000-01-03   -0.059786
2000-01-06    0.836517
2000-01-09    0.688500
dtype: float64

In [169]: ts2.reindex(ts.index)
Out[169]: 
2000-01-03   -0.059786
2000-01-04         NaN
2000-01-05         NaN
2000-01-06    0.836517
2000-01-07         NaN
2000-01-08         NaN
2000-01-09    0.688500
2000-01-10         NaN
Freq: D, dtype: float64

In [170]: ts2.reindex(ts.index, method='ffill')
Out[170]: 
2000-01-03   -0.059786
2000-01-04   -0.059786
2000-01-05   -0.059786
2000-01-06    0.836517
2000-01-07    0.836517
2000-01-08    0.836517
2000-01-09    0.688500
2000-01-10    0.688500
Freq: D, dtype: float64

In [171]: ts2.reindex(ts.index, method='bfill')
Out[171]: 
2000-01-03   -0.059786
2000-01-04    0.836517
2000-01-05    0.836517
2000-01-06    0.836517
2000-01-07    0.688500
2000-01-08    0.688500
2000-01-09    0.688500
2000-01-10         NaN
Freq: D, dtype: float64

Note these methods require that the indexes are order increasing.

Note the same result could have been achieved using fillna:

In [172]: ts2.reindex(ts.index).fillna(method='ffill')
Out[172]: 
2000-01-03   -0.059786
2000-01-04   -0.059786
2000-01-05   -0.059786
2000-01-06    0.836517
2000-01-07    0.836517
2000-01-08    0.836517
2000-01-09    0.688500
2000-01-10    0.688500
Freq: D, dtype: float64

Note that reindex will raise a ValueError if the index is not monotonic. fillna will not make any checks on the order of the index.

Dropping labels from an axis

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

In [173]: df
Out[173]: 
        one     three       two
a -0.701368       NaN -0.087103
b  0.109333 -0.354359  0.637674
c -0.231617 -0.148387 -0.002666
d       NaN -0.167407  0.104044

[4 rows x 3 columns]

In [174]: df.drop(['a', 'd'], axis=0)
Out[174]: 
        one     three       two
b  0.109333 -0.354359  0.637674
c -0.231617 -0.148387 -0.002666

[2 rows x 3 columns]

In [175]: df.drop(['one'], axis=1)
Out[175]: 
      three       two
a       NaN -0.087103
b -0.354359  0.637674
c -0.148387 -0.002666
d -0.167407  0.104044

[4 rows x 2 columns]

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

In [176]: df.reindex(df.index - ['a', 'd'])
Out[176]: 
        one     three       two
b  0.109333 -0.354359  0.637674
c -0.231617 -0.148387 -0.002666

[2 rows x 3 columns]

Renaming / mapping labels

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

In [177]: s
Out[177]: 
a    0.479090
b    0.686579
c   -0.949750
d   -0.257472
e   -0.568459
dtype: float64

In [178]: s.rename(str.upper)
Out[178]: 
A    0.479090
B    0.686579
C   -0.949750
D   -0.257472
E   -0.568459
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). But if you pass a dict or Series, it need only contain a subset of the labels as keys:

In [179]: df.rename(columns={'one' : 'foo', 'two' : 'bar'},
   .....:           index={'a' : 'apple', 'b' : 'banana', 'd' : 'durian'})
   .....: 
Out[179]: 
             foo     three       bar
apple  -0.701368       NaN -0.087103
banana  0.109333 -0.354359  0.637674
c      -0.231617 -0.148387 -0.002666
durian       NaN -0.167407  0.104044

[4 rows x 3 columns]

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.

The Panel class has a related rename_axis class which can rename any of its three axes.

Iteration

Because Series is array-like, basic iteration produces the values. Other data structures follow the dict-like convention of iterating over the “keys” of the objects. In short:

• Series: values
• DataFrame: column labels
• Panel: item labels

Thus, for example:

In [180]: for col in df:
   .....:     print(col)
   .....: 
one
three
two

iteritems

Consistent with the dict-like interface, iteritems iterates through key-value pairs:

• Series: (index, scalar value) pairs
• DataFrame: (column, Series) pairs
• Panel: (item, DataFrame) pairs

For example:

In [181]: for item, frame in wp.iteritems():
   .....:     print(item)
   .....:     print(frame)
   .....: 
Item1
                   A         B         C         D
2000-01-01 -1.118121  0.431279  0.554724 -1.333649
2000-01-02 -0.332174 -0.485882  1.725945  1.799276
2000-01-03 -0.968916 -0.779465 -2.000701 -1.866630
2000-01-04 -1.101268  1.957478  0.058889  0.758071
2000-01-05  0.076612 -0.548502 -0.160485 -0.377780

[5 rows x 4 columns]
Item2
                   A         B         C         D
2000-01-01  0.249911 -0.341270 -0.272599 -0.277446
2000-01-02 -1.102896  0.100307 -1.602814  0.920139
2000-01-03 -0.643870  0.060336 -0.434942 -0.494305
2000-01-04  0.737973  0.451632  0.334124 -0.787062
2000-01-05  0.651396 -0.741919  1.193881 -2.395763

[5 rows x 4 columns]

iterrows

New in v0.7 is the ability to iterate efficiently through rows of a DataFrame. It returns an iterator yielding each index value along with a Series containing the data in each row:

In [182]: for row_index, row in df2.iterrows():
   .....:     print('%s\n%s' % (row_index, row))
   .....: 
a
one   -0.701368
two   -0.087103
Name: a, dtype: float64
b
one    0.109333
two    0.637674
Name: b, dtype: float64
c
one   -0.231617
two   -0.002666
Name: c, dtype: float64

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

In [183]: df2 = DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})

In [184]: print(df2)
   x  y
0  1  4
1  2  5
2  3  6

[3 rows x 2 columns]

In [185]: print(df2.T)
   0  1  2
x  1  2  3
y  4  5  6

[2 rows x 3 columns]

In [186]: df2_t = DataFrame(dict((idx,values) for idx, values in df2.iterrows()))

In [187]: print(df2_t)
   0  1  2
x  1  2  3
y  4  5  6

[2 rows x 3 columns]

Note

iterrows does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,

In [188]: df_iter = DataFrame([[1, 1.0]], columns=['x', 'y'])

In [189]: row = next(df_iter.iterrows())[1]

In [190]: print(row['x'].dtype)
float64

In [191]: print(df_iter['x'].dtype)
int64

itertuples

This method will return an iterator yielding a tuple 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 proper.

For instance,

In [192]: for r in df2.itertuples():
   .....:     print(r)
   .....: 
(0, 1, 4)
(1, 2, 5)
(2, 3, 6)

Vectorized string methods

Series is equipped (as of pandas 0.8.1) 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) build-in string methods:

Splitting and Replacing Strings

In [193]: s = Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])

In [194]: s.str.lower()
Out[194]: 
0       a
1       b
2       c
3    aaba
4    baca
5     NaN
6    caba
7     dog
8     cat
dtype: object

In [195]: s.str.upper()
Out[195]: 
0       A
1       B
2       C
3    AABA
4    BACA
5     NaN
6    CABA
7     DOG
8     CAT
dtype: object

In [196]: s.str.len()
Out[196]: 
0     1
1     1
2     1
3     4
4     4
5   NaN
6     4
7     3
8     3
dtype: float64

Methods like split return a Series of lists:

In [197]: s2 = Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h'])

In [198]: s2.str.split('_')
Out[198]: 
0    [a, b, c]
1    [c, d, e]
2          NaN
3    [f, g, h]
dtype: object

Elements in the split lists can be accessed using get or [] notation:

In [199]: s2.str.split('_').str.get(1)
Out[199]: 
0      b
1      d
2    NaN
3      g
dtype: object

In [200]: s2.str.split('_').str[1]
Out[200]: 
0      b
1      d
2    NaN
3      g
dtype: object

Methods like replace and findall take regular expressions, too:

In [201]: s3 = Series(['A', 'B', 'C', 'Aaba', 'Baca',
   .....:             '', np.nan, 'CABA', 'dog', 'cat'])
   .....: 

In [202]: s3
Out[202]: 
0       A
1       B
2       C
3    Aaba
4    Baca
5        
6     NaN
7    CABA
8     dog
9     cat
dtype: object

In [203]: s3.str.replace('^.a|dog', 'XX-XX ', case=False)
Out[203]: 
0           A
1           B
2           C
3    XX-XX ba
4    XX-XX ca
5            
6         NaN
7    XX-XX BA
8      XX-XX 
9     XX-XX t
dtype: object

Extracting Substrings

The method extract (introduced in version 0.13) accepts regular expressions with match groups. Extracting a regular expression with one group returns a Series of strings.

In [204]: Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)')
Out[204]: 
0      1
1      2
2    NaN
dtype: object

Elements that do not match return NaN. Extracting a regular expression with more than one group returns a DataFrame with one column per group.

In [205]: Series(['a1', 'b2', 'c3']).str.extract('([ab])(\d)')
Out[205]: 
     0    1
0    a    1
1    b    2
2  NaN  NaN

[3 rows x 2 columns]

Elements that do not match return a row filled with NaN. Thus, a Series of messy strings can be “converted” into a like-indexed Series or DataFrame of cleaned-up or more useful strings, without necessitating get() to access tuples or re.match objects.

Named groups like

In [206]: Series(['a1', 'b2', 'c3']).str.extract('(?P<letter>[ab])(?P<digit>\d)')
Out[206]: 
  letter digit
0      a     1
1      b     2
2    NaN   NaN

[3 rows x 2 columns]

and optional groups like

In [207]: Series(['a1', 'b2', '3']).str.extract('(?P<letter>[ab])?(?P<digit>\d)')
Out[207]: 
  letter digit
0      a     1
1      b     2
2    NaN     3

[3 rows x 2 columns]

can also be used.

Testing for Strings that Match or Contain a Pattern

You can check whether elements contain a pattern:

In [208]: pattern = r'[a-z][0-9]'

In [209]: Series(['1', '2', '3a', '3b', '03c']).str.contains(pattern)
Out[209]: 
0    False
1    False
2    False
3    False
4    False
dtype: bool

or match a pattern:

In [210]: Series(['1', '2', '3a', '3b', '03c']).str.match(pattern, as_indexer=True)
Out[210]: 
0    False
1    False
2    False
3    False
4    False
dtype: bool

The distinction between match and contains is strictness: match relies on strict re.match, while contains relies on re.search.

Warning

In previous versions, match was for extracting groups, returning a not-so-convenient Series of tuples. The new method extract (described in the previous section) is now preferred.

This old, deprecated behavior of match is still the default. As demonstrated above, use the new behavior by setting as_indexer=True. In this mode, match is analogous to contains, returning a boolean Series. The new behavior will become the default behavior in a future release.

Methods like match, contains, startswith, and endswith take

an extra na argument so missing values can be considered True or False:

In [211]: s4 = Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])

In [212]: s4.str.contains('A', na=False)
Out[212]: 
0     True
1    False
2    False
3     True
4    False
5    False
6     True
7    False
8    False
dtype: bool

Method	Description
cat	Concatenate strings
split	Split strings on delimiter
get	Index into each element (retrieve i-th element)
join	Join strings in each element of the Series with passed separator
contains	Return boolean array if each string contains pattern/regex
replace	Replace occurrences of pattern/regex with some other string
repeat	Duplicate values (s.str.repeat(3) equivalent to x * 3)
pad	Add whitespace to left, right, or both sides of strings
center	Equivalent to pad(side='both')
slice	Slice each string in the Series
slice_replace	Replace slice in each string with passed value
count	Count occurrences of pattern
startswith	Equivalent to str.startswith(pat) for each element
endswith	Equivalent to str.endswith(pat) for each element
findall	Compute list of all occurrences of pattern/regex for each string
match	Call re.match on each element, returning matched groups as list
extract	Call re.match on each element, as match does, but return matched groups as strings for convenience.
len	Compute string lengths
strip	Equivalent to str.strip
rstrip	Equivalent to str.rstrip
lstrip	Equivalent to str.lstrip
lower	Equivalent to str.lower
upper	Equivalent to str.upper

Getting indicator variables from seperated strings

You can extract dummy variables from string columns. For example if they are seperated by a '|':

In [213]: s = pd.Series(['a', 'a|b', np.nan, 'a|c'])

In [214]: s.str.get_dummies(sep='|')
Out[214]: 
   a  b  c
0  1  0  0
1  1  1  0
2  0  0  0
3  1  0  1

[4 rows x 3 columns]

See also get_dummies().

Sorting by index and value

There are two obvious kinds of sorting that you may be interested in: sorting by label and sorting by actual values. The primary method for sorting axis labels (indexes) across data structures is the sort_index method.

In [215]: unsorted_df = df.reindex(index=['a', 'd', 'c', 'b'],
   .....:                          columns=['three', 'two', 'one'])
   .....: 

In [216]: unsorted_df.sort_index()
Out[216]: 
      three       two       one
a       NaN -0.087103 -0.701368
b -0.354359  0.637674  0.109333
c -0.148387 -0.002666 -0.231617
d -0.167407  0.104044       NaN

[4 rows x 3 columns]

In [217]: unsorted_df.sort_index(ascending=False)
Out[217]: 
      three       two       one
d -0.167407  0.104044       NaN
c -0.148387 -0.002666 -0.231617
b -0.354359  0.637674  0.109333
a       NaN -0.087103 -0.701368

[4 rows x 3 columns]

In [218]: unsorted_df.sort_index(axis=1)
Out[218]: 
        one     three       two
a -0.701368       NaN -0.087103
d       NaN -0.167407  0.104044
c -0.231617 -0.148387 -0.002666
b  0.109333 -0.354359  0.637674

[4 rows x 3 columns]

DataFrame.sort_index can accept an optional by argument for axis=0 which will use an arbitrary vector or a column name of the DataFrame to determine the sort order:

In [219]: df1 = DataFrame({'one':[2,1,1,1],'two':[1,3,2,4],'three':[5,4,3,2]})

In [220]: df1.sort_index(by='two')
Out[220]: 
   one  three  two
0    2      5    1
2    1      3    2
1    1      4    3
3    1      2    4

[4 rows x 3 columns]

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

In [221]: df1[['one', 'two', 'three']].sort_index(by=['one','two'])
Out[221]: 
   one  two  three
2    1    2      3
1    1    3      4
3    1    4      2
0    2    1      5

[4 rows x 3 columns]

Series has the method order (analogous to R’s order function) which sorts by value, with special treatment of NA values via the na_last argument:

In [222]: s[2] = np.nan

In [223]: s.order()
Out[223]: 
0      a
1    a|b
3    a|c
2    NaN
dtype: object

In [224]: s.order(na_last=False)
Out[224]: 
2    NaN
0      a
1    a|b
3    a|c
dtype: object

Some other sorting notes / nuances:

• Series.sort sorts a Series by value in-place. This is to provide compatibility with NumPy methods which expect the ndarray.sort behavior.
• DataFrame.sort takes a column argument instead of by. This method will likely be deprecated in a future release in favor of just using sort_index.

Copying

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

• Inserting, deleting, or modifying a column
• Assigning to the index or columns attributes
• For homogeneous data, directly modifying the values via the values attribute or advanced indexing

To be clear, no pandas methods have the side effect of modifying your data; almost all methods return new objects, leaving the original object untouched. If data is modified, it is because you did so explicitly.

dtypes

The main types stored in pandas objects are float, int, bool, datetime64[ns], timedelta[ns], and object. In addition these dtypes have item sizes, e.g. int64 and int32. A convenient dtypes attribute for DataFrames returns a Series with the data type of each column.

In [225]: dft = DataFrame(dict( A = np.random.rand(3),
   .....:                       B = 1,
   .....:                       C = 'foo',
   .....:                       D = Timestamp('20010102'),
   .....:                       E = Series([1.0]*3).astype('float32'),
   .....:                       F = False,
   .....:                       G = Series([1]*3,dtype='int8')))
   .....: 

In [226]: dft
Out[226]: 
          A  B    C          D  E      F  G
0  0.298496  1  foo 2001-01-02  1  False  1
1  0.347135  1  foo 2001-01-02  1  False  1
2  0.141330  1  foo 2001-01-02  1  False  1

[3 rows x 7 columns]

In [227]: dft.dtypes
Out[227]: 
A           float64
B             int64
C            object
D    datetime64[ns]
E           float32
F              bool
G              int8
dtype: object

On a Series use the dtype method.

In [228]: dft['A'].dtype
Out[228]: dtype('float64')

If a pandas object contains data 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 [229]: Series([1, 2, 3, 4, 5, 6.])
Out[229]: 
0    1
1    2
2    3
3    4
4    5
5    6
dtype: float64

# string data forces an ``object`` dtype
In [230]: Series([1, 2, 3, 6., 'foo'])
Out[230]: 
0      1
1      2
2      3
3      6
4    foo
dtype: object

The method get_dtype_counts will return the number of columns of each type in a DataFrame:

In [231]: dft.get_dtype_counts()
Out[231]: 
bool              1
datetime64[ns]    1
float32           1
float64           1
int64             1
int8              1
object            1
dtype: int64

Numeric dtypes will propagate and can coexist in DataFrames (starting in v0.11.0). If a dtype is passed (either directly via the dtype keyword, a passed ndarray, or a passed Series, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will NOT be combined. The following example will give you a taste.

In [232]: df1 = DataFrame(randn(8, 1), columns = ['A'], dtype = 'float32')

In [233]: df1
Out[233]: 
          A
0  1.111528
1 -1.189947
2 -0.652389
3 -0.216515
4 -2.163590
5  0.117168
6 -1.806383
7 -0.249113

[8 rows x 1 columns]

In [234]: df1.dtypes
Out[234]: 
A    float32
dtype: object

In [235]: df2 = DataFrame(dict( A = Series(randn(8),dtype='float16'),
   .....:                       B = Series(randn(8)),
   .....:                       C = Series(np.array(randn(8),dtype='uint8')) ))
   .....: 

In [236]: df2
Out[236]: 
          A         B    C
0  1.109375 -0.265597    0
1  0.748535 -1.841820    0
2 -1.319336 -0.661921    0
3  2.005859  1.198778    0
4  0.260498  0.138315  254
5  1.785156  0.967672    0
6  0.481689  0.447494    2
7  0.319336 -0.361367    2

[8 rows x 3 columns]

In [237]: df2.dtypes
Out[237]: 
A    float16
B    float64
C      uint8
dtype: object

defaults

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

In [238]: DataFrame([1, 2], columns=['a']).dtypes
Out[238]: 
a    int64
dtype: object

In [239]: DataFrame({'a': [1, 2]}).dtypes
Out[239]: 
a    int64
dtype: object

In [240]: DataFrame({'a': 1 }, index=list(range(2))).dtypes
Out[240]: 
a    int64
dtype: object

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

In [241]: frame = DataFrame(np.array([1, 2]))

upcasting

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

In [242]: df3 = df1.reindex_like(df2).fillna(value=0.0) + df2

In [243]: df3
Out[243]: 
          A         B    C
0  2.220903 -0.265597    0
1 -0.441412 -1.841820    0
2 -1.971725 -0.661921    0
3  1.789344  1.198778    0
4 -1.903092  0.138315  254
5  1.902324  0.967672    0
6 -1.324694  0.447494    2
7  0.070223 -0.361367    2

[8 rows x 3 columns]

In [244]: df3.dtypes
Out[244]: 
A    float32
B    float64
C    float64
dtype: object

The values attribute on a DataFrame return the lower-common-denominator of the dtypes, meaning the dtype that can accommodate ALL of the types in the resulting homogenous dtyped numpy array. This can force some upcasting.

In [245]: df3.values.dtype
Out[245]: dtype('float64')

astype

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

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

In [246]: df3
Out[246]: 
          A         B    C
0  2.220903 -0.265597    0
1 -0.441412 -1.841820    0
2 -1.971725 -0.661921    0
3  1.789344  1.198778    0
4 -1.903092  0.138315  254
5  1.902324  0.967672    0
6 -1.324694  0.447494    2
7  0.070223 -0.361367    2

[8 rows x 3 columns]

In [247]: df3.dtypes
Out[247]: 
A    float32
B    float64
C    float64
dtype: object

# conversion of dtypes
In [248]: df3.astype('float32').dtypes
Out[248]: 
A    float32
B    float32
C    float32
dtype: object

object conversion

convert_objects is a method to try to force conversion of types from the object dtype to other types. To force conversion of specific types that are number like, e.g. could be a string that represents a number, pass convert_numeric=True. This will force strings and numbers alike to be numbers if possible, otherwise they will be set to np.nan.

In [249]: df3['D'] = '1.'

In [250]: df3['E'] = '1'

In [251]: df3.convert_objects(convert_numeric=True).dtypes
Out[251]: 
A    float32
B    float64
C    float64
D    float64
E      int64
dtype: object

# same, but specific dtype conversion
In [252]: df3['D'] = df3['D'].astype('float16')

In [253]: df3['E'] = df3['E'].astype('int32')

In [254]: df3.dtypes
Out[254]: 
A    float32
B    float64
C    float64
D    float16
E      int32
dtype: object

To force conversion to datetime64[ns], pass convert_dates='coerce'. This will convert any datetime-like object to dates, forcing other values to NaT. This might be useful if you are reading in data which is mostly dates, but occasionally has non-dates intermixed and you want to represent as missing.

In [255]: s = Series([datetime(2001,1,1,0,0),
   .....:            'foo', 1.0, 1, Timestamp('20010104'),
   .....:            '20010105'],dtype='O')
   .....: 

In [256]: s
Out[256]: 
0    2001-01-01 00:00:00
1                    foo
2                      1
3                      1
4    2001-01-04 00:00:00
5               20010105
dtype: object

In [257]: s.convert_objects(convert_dates='coerce')
Out[257]: 
0   2001-01-01
1          NaT
2          NaT
3          NaT
4   2001-01-04
5   2001-01-05
dtype: datetime64[ns]

In addition, convert_objects will attempt the soft conversion of any object dtypes, meaning that if all the objects in a Series are of the same type, the Series will have that dtype.

gotchas

Performing selection operations on integer type data can easily upcast the data to floating. The dtype of the input data will be preserved in cases where nans are not introduced (starting in 0.11.0) See also integer na gotchas

In [258]: dfi = df3.astype('int32')

In [259]: dfi['E'] = 1

In [260]: dfi
Out[260]: 
   A  B    C  D  E
0  2  0    0  1  1
1  0 -1    0  1  1
2 -1  0    0  1  1
3  1  1    0  1  1
4 -1  0  254  1  1
5  1  0    0  1  1
6 -1  0    2  1  1
7  0  0    2  1  1

[8 rows x 5 columns]

In [261]: dfi.dtypes
Out[261]: 
A    int32
B    int32
C    int32
D    int32
E    int64
dtype: object

In [262]: casted = dfi[dfi>0]

In [263]: casted
Out[263]: 
    A   B    C  D  E
0   2 NaN  NaN  1  1
1 NaN NaN  NaN  1  1
2 NaN NaN  NaN  1  1
3   1   1  NaN  1  1
4 NaN NaN  254  1  1
5   1 NaN  NaN  1  1
6 NaN NaN    2  1  1
7 NaN NaN    2  1  1

[8 rows x 5 columns]

In [264]: casted.dtypes
Out[264]: 
A    float64
B    float64
C    float64
D      int32
E      int64
dtype: object

While float dtypes are unchanged.

In [265]: dfa = df3.copy()

In [266]: dfa['A'] = dfa['A'].astype('float32')

In [267]: dfa.dtypes
Out[267]: 
A    float32
B    float64
C    float64
D    float16
E      int32
dtype: object

In [268]: casted = dfa[df2>0]

In [269]: casted
Out[269]: 
          A         B    C   D   E
0  2.220903       NaN  NaN NaN NaN
1 -0.441412       NaN  NaN NaN NaN
2       NaN       NaN  NaN NaN NaN
3  1.789344  1.198778  NaN NaN NaN
4 -1.903092  0.138315  254 NaN NaN
5  1.902324  0.967672  NaN NaN NaN
6 -1.324694  0.447494    2 NaN NaN
7  0.070223       NaN    2 NaN NaN

[8 rows x 5 columns]

In [270]: casted.dtypes
Out[270]: 
A    float32
B    float64
C    float64
D    float16
E    float64
dtype: object

Working with package options

New in version 0.10.1.

Pandas has an options system that let’s you customize some aspects of it’s behaviour, display-related options being those the user is must likely to adjust.

Options have a full “dotted-style”, case-insensitive name (e.g. display.max_rows), You can get/set options directly as attributes of the top-level options attribute:

In [271]: import pandas as pd

In [272]: pd.options.display.max_rows
Out[272]: 15

In [273]: pd.options.display.max_rows = 999

In [274]: pd.options.display.max_rows
Out[274]: 999

There is also an API composed of 4 relevant functions, available directly from the pandas namespace, and they are:

• get_option / set_option - get/set the value of a single option.
• reset_option - reset one or more options to their default value.
• describe_option - print the descriptions of one or more options.

Note: developers can check out pandas/core/config.py for more info.

All of the functions above accept a regexp pattern (re.search style) as an argument, and so passing in a substring will work - as long as it is unambiguous :

In [275]: get_option("display.max_rows")
Out[275]: 999

In [276]: set_option("display.max_rows",101)

In [277]: get_option("display.max_rows")
Out[277]: 101

In [278]: set_option("max_r",102)

In [279]: get_option("display.max_rows")
Out[279]: 102

The following will not work because it matches multiple option names, e.g. display.max_colwidth, display.max_rows, display.max_columns:

In [280]: try:
   .....:     get_option("display.max_")
   .....: except KeyError as e:
   .....:     print(e)
   .....: 
'Pattern matched multiple keys'

Note: Using this form of shorthand may cause your code to break if new options with similar names are added in future versions.

You can get a list of available options and their descriptions with describe_option. When called with no argument describe_option will print out the descriptions for all available options.

In [281]: describe_option()
display.chop_threshold: [default: None] [currently: None]
: float or None
        if set to a float value, all float values smaller then the given threshold
        will be displayed as exactly 0 by repr and friends.
display.colheader_justify: [default: right] [currently: right]
: 'left'/'right'
        Controls the justification of column headers. used by DataFrameFormatter.
display.column_space: [default: 12] [currently: 12]No description available.

display.date_dayfirst: [default: False] [currently: False]
: boolean
        When True, prints and parses dates with the day first, eg 20/01/2005
display.date_yearfirst: [default: False] [currently: False]
: boolean
        When True, prints and parses dates with the year first, eg 2005/01/20
display.encoding: [default: UTF-8] [currently: UTF-8]
: str/unicode
        Defaults to the detected encoding of the console.
        Specifies the encoding to be used for strings returned by to_string,
        these are generally strings meant to be displayed on the console.
display.expand_frame_repr: [default: True] [currently: True]
: boolean
        Whether to print out the full DataFrame repr for wide DataFrames across
        multiple lines, `max_columns` is still respected, but the output will
        wrap-around across multiple "pages" if it's width exceeds `display.width`.
display.float_format: [default: None] [currently: None]
: callable
        The callable should accept a floating point number and return
        a string with the desired format of the number. This is used
        in some places like SeriesFormatter.
        See core.format.EngFormatter for an example.
display.height: [default: 60] [currently: 60]
: int
        Deprecated.
	(Deprecated, use `display.max_rows` instead.)

display.large_repr: [default: truncate] [currently: truncate]
: 'truncate'/'info'
    
        For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can
        show a truncated table (the default from 0.13), or switch to the view from
        df.info() (the behaviour in earlier versions of pandas).
display.line_width: [default: 80] [currently: 80]
: int
        Deprecated.
	(Deprecated, use `display.width` instead.)

display.max_columns: [default: 20] [currently: 20]
: int
        max_rows and max_columns are used in __repr__() methods to decide if
        to_string() or info() is used to render an object to a string.  In case
        python/IPython is running in a terminal this can be set to 0 and pandas
        will correctly auto-detect the width the terminal and swap to a smaller
        format in case all columns would not fit vertically. The IPython notebook,
        IPython qtconsole, or IDLE do not run in a terminal and hence it is not
        possible to do correct auto-detection.
        'None' value means unlimited.
display.max_colwidth: [default: 50] [currently: 50]
: int
        The maximum width in characters of a column in the repr of
        a pandas data structure. When the column overflows, a "..."
        placeholder is embedded in the output.
display.max_info_columns: [default: 100] [currently: 100]
: int
        max_info_columns is used in DataFrame.info method to decide if
        per column information will be printed.
display.max_info_rows: [default: 1690785] [currently: 1690785]
: int or None
        df.info() will usually show null-counts for each column.
        For large frames this can be quite slow. max_info_rows and max_info_cols
        limit this null check only to frames with smaller dimensions then specified.
display.max_rows: [default: 60] [currently: 60]
: int
        This sets the maximum number of rows pandas should output when printing
        out various output. For example, this value determines whether the repr()
        for a dataframe prints out fully or just a summary repr.
        'None' value means unlimited.
display.max_seq_items: [default: 100] [currently: 100]
: int or None
    
        when pretty-printing a long sequence, no more then `max_seq_items`
        will be printed. If items are omitted, they will be denoted by the
        addition of "..." to the resulting string.
    
        If set to None, the number of items to be printed is unlimited.
display.mpl_style: [default: None] [currently: None]
: bool
    
        Setting this to 'default' will modify the rcParams used by matplotlib
        to give plots a more pleasing visual style by default.
        Setting this to None/False restores the values to their initial value.
display.multi_sparse: [default: True] [currently: True]
: boolean
        "sparsify" MultiIndex display (don't display repeated
        elements in outer levels within groups)
display.notebook_repr_html: [default: True] [currently: True]
: boolean
        When True, IPython notebook will use html representation for
        pandas objects (if it is available).
display.pprint_nest_depth: [default: 3] [currently: 3]
: int
        Controls the number of nested levels to process when pretty-printing
display.precision: [default: 7] [currently: 7]
: int
        Floating point output precision (number of significant digits). This is
        only a suggestion
display.show_dimensions: [default: True] [currently: True]
: boolean
        Whether to print out dimensions at the end of DataFrame repr.
display.width: [default: 80] [currently: 80]
: int
        Width of the display in characters. In case python/IPython is running in
        a terminal this can be set to None and pandas will correctly auto-detect
        the width.
        Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a
        terminal and hence it is not possible to correctly detect the width.
io.excel.xls.writer: [default: xlwt] [currently: xlwt]
: string
        The default Excel writer engine for 'xls' files. Available options:
        'xlwt' (the default).
io.excel.xlsm.writer: [default: openpyxl] [currently: openpyxl]
: string
        The default Excel writer engine for 'xlsm' files. Available options:
        'openpyxl' (the default).
io.excel.xlsx.writer: [default: xlsxwriter] [currently: xlsxwriter]
: string
        The default Excel writer engine for 'xlsx' files. Available options:
        'xlsxwriter' (the default), 'openpyxl'.
io.hdf.default_format: [default: None] [currently: None]
: format
        default format writing format, if None, then
        put will default to 'fixed' and append will default to 'table'
io.hdf.dropna_table: [default: True] [currently: True]
: boolean
        drop ALL nan rows when appending to a table
mode.chained_assignment: [default: warn] [currently: warn]
: string
        Raise an exception, warn, or no action if trying to use chained assignment,
        The default is warn
mode.sim_interactive: [default: False] [currently: False]
: boolean
        Whether to simulate interactive mode for purposes of testing
mode.use_inf_as_null: [default: False] [currently: False]
: boolean
        True means treat None, NaN, INF, -INF as null (old way),
        False means None and NaN are null, but INF, -INF are not null
        (new way).

or you can get the description for just the options that match the regexp you pass in:

In [282]: describe_option("date")
display.date_dayfirst: [default: False] [currently: False]
: boolean
        When True, prints and parses dates with the day first, eg 20/01/2005
display.date_yearfirst: [default: False] [currently: False]
: boolean
        When True, prints and parses dates with the year first, eg 2005/01/20

All options also have a default value, and you can use the reset_option to do just that:

In [283]: get_option("display.max_rows")
Out[283]: 60

In [284]: set_option("display.max_rows",999)

In [285]: get_option("display.max_rows")
Out[285]: 999

In [286]: reset_option("display.max_rows")

In [287]: get_option("display.max_rows")
Out[287]: 60

It’s also possible to reset multiple options at once (using a regex):

In [288]: reset_option("^display")

New in version 0.13.1.

In [289]: with option_context("display.max_rows",10,"display.max_columns", 5):
   .....:    print get_option("display.max_rows")
   .....: 
10

In [290]: print get_option("display.max_columns")
20

In [291]: print get_option("display.max_rows")
60

In [292]: print get_option("display.max_columns")
20

Console Output Formatting

Use the set_eng_float_format function in the pandas.core.common module to alter the floating-point formatting of pandas objects to produce a particular format.

For instance:

In [293]: set_eng_float_format(accuracy=3, use_eng_prefix=True)

In [294]: s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])

In [295]: s/1.e3
Out[295]: 
a   -184.526u
b   -615.011u
c    104.861u
d   -524.434u
e      5.385u
dtype: float64

In [296]: s/1.e6
Out[296]: 
a   -184.526n
b   -615.011n
c    104.861n
d   -524.434n
e      5.385n
dtype: float64

The set_printoptions function has a number of options for controlling how floating point numbers are formatted (using the precision argument) in the console and . The max_rows and max_columns control how many rows and columns of DataFrame objects are shown by default. If max_columns is set to 0 (the default, in fact), the library will attempt to fit the DataFrame’s string representation into the current terminal width, and defaulting to the summary view otherwise.