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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    0.438139
1    1.360585
2   -1.225777
3    0.753771
4    0.776008

In [7]: long_series.tail(3)
Out[7]: 
997    0.642535
998   -0.677004
999    0.518081

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 -0.02034 -0.154453 -0.086596
2000-01-02  0.25638  1.443283 -1.303983

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

In [10]: df
Out[10]: 
                   a         b         c
2000-01-01 -0.020340 -0.154453 -0.086596
2000-01-02  0.256380  1.443283 -1.303983
2000-01-03  1.197449 -1.618188 -0.049346
2000-01-04  0.277269  1.056039  0.235826
2000-01-05  0.765297  0.248318 -1.864354
2000-01-06 -1.139876 -0.517559 -1.248581
2000-01-07  0.558149  0.375893 -0.045086
2000-01-08 -0.242394  1.135132  0.162421

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

In [11]: s.values
Out[11]: array([-1.306 ,  0.4286, -0.4525, -0.9383,  1.12  ])

In [12]: df.values
Out[12]: 
array([[-0.0203, -0.1545, -0.0866],
       [ 0.2564,  1.4433, -1.304 ],
       [ 1.1974, -1.6182, -0.0493],
       [ 0.2773,  1.056 ,  0.2358],
       [ 0.7653,  0.2483, -1.8644],
       [-1.1399, -0.5176, -1.2486],
       [ 0.5581,  0.3759, -0.0451],
       [-0.2424,  1.1351,  0.1624]])

In [13]: wp.values
Out[13]: 
array([[[-0.6449,  0.3085,  0.5504,  0.6853],
        [ 1.5955, -0.8558, -0.3944,  0.4166],
        [-0.2361, -0.2984, -0.8515,  0.3459],
        [ 0.5635,  0.8122, -1.4808,  1.1536],
        [ 2.4158,  1.1638, -0.5999, -0.5923]],
       [[ 0.0613,  0.67  ,  1.3327,  1.4705],
        [-0.3537,  0.8369, -0.5382, -0.8507],
        [-0.1388,  0.2169,  2.38  , -0.3702],
        [-0.2116,  0.329 , -0.4283,  1.3678],
        [-1.1713, -1.5828, -0.0008, -2.2713]]])

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.

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 = DataFrame(d)

In [16]: df
Out[16]: 
        one     three       two
a  0.611941       NaN -0.693293
b -1.525132  0.792016  1.011179
c  0.307937  0.307100 -0.570476
d       NaN  1.746630 -1.182688

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

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

In [19]: df.sub(row, axis='columns')
Out[19]: 
        one     three       two
a  2.137073       NaN -1.704472
b  0.000000  0.000000  0.000000
c  1.833068 -0.484916 -1.581655
d       NaN  0.954614 -2.193867

In [20]: df.sub(row, axis=1)
Out[20]: 
        one     three       two
a  2.137073       NaN -1.704472
b  0.000000  0.000000  0.000000
c  1.833068 -0.484916 -1.581655
d       NaN  0.954614 -2.193867

In [21]: df.sub(column, axis='index')
Out[21]: 
        one     three  two
a  1.305234       NaN    0
b -2.536311 -0.219164    0
c  0.878413  0.877576    0
d       NaN  2.929318    0

In [22]: df.sub(column, axis=0)
Out[22]: 
        one     three  two
a  1.305234       NaN    0
b -2.536311 -0.219164    0
c  0.878413  0.877576    0
d       NaN  2.929318    0

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  0.738798 -0.362797
B  0.226041  0.093998
C -0.555252  0.549084
D  0.401836 -0.130776

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  0.611941       NaN -0.693293
b -1.525132  0.792016  1.011179
c  0.307937  0.307100 -0.570476
d       NaN  1.746630 -1.182688

In [27]: df2
Out[27]: 
        one     three       two
a  0.611941  1.000000 -0.693293
b -1.525132  0.792016  1.011179
c  0.307937  0.307100 -0.570476
d       NaN  1.746630 -1.182688

In [28]: df + df2
Out[28]: 
        one     three       two
a  1.223882       NaN -1.386585
b -3.050263  1.584032  2.022359
c  0.615874  0.614201 -1.140951
d       NaN  3.493260 -2.365376

In [29]: df.add(df2, fill_value=0)
Out[29]: 
        one     three       two
a  1.223882  1.000000 -1.386585
b -3.050263  1.584032  2.022359
c  0.615874  0.614201 -1.140951
d       NaN  3.493260 -2.365376

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

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

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

In [33]: df2 = DataFrame({'A' : [5., 2., 4., np.nan, 3., 7.],
   ....:                  'B' : [np.nan, np.nan, 3., 4., 6., 8.]})
   ....:

In [34]: df1
Out[34]: 
    A   B
0   1 NaN
1 NaN   2
2   3   3
3   5 NaN
4 NaN   6

In [35]: df2
Out[35]: 
    A   B
0   5 NaN
1   2 NaN
2   4   3
3 NaN   4
4   3   6
5   7   8

In [36]: df1.combine_first(df2)
Out[36]: 
   A   B
0  1 NaN
1  2   2
2  3   3
3  5   4
4  3   6
5  7   8

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

In [38]: df1.combine(df2, combiner)
Out[38]: 
   A   B
0  1 NaN
1  2   2
2  3   3
3  5   4
4  3   6
5  7   8

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 [39]: df
Out[39]: 
        one     three       two
a  0.611941       NaN -0.693293
b -1.525132  0.792016  1.011179
c  0.307937  0.307100 -0.570476
d       NaN  1.746630 -1.182688

In [40]: df.mean(0)
Out[40]: 
one     -0.201751
three    0.948582
two     -0.358819

In [41]: df.mean(1)
Out[41]: 
a   -0.040676
b    0.092688
c    0.014854
d    0.281971

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

In [42]: df.sum(0, skipna=False)
Out[42]: 
one           NaN
three         NaN
two     -1.435277

In [43]: df.sum(axis=1, skipna=True)
Out[43]: 
a   -0.081352
b    0.278064
c    0.044561
d    0.563942

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

In [45]: ts_stand.std()
Out[45]: 
one      1
three    1
two      1

In [46]: xs_stand = df.sub(df.mean(1), axis=0).div(df.std(1), axis=0)

In [47]: xs_stand.std(1)
Out[47]: 
a    1
b    1
c    1
d    1

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

In [48]: df.cumsum()
Out[48]: 
        one     three       two
a  0.611941       NaN -0.693293
b -0.913191  0.792016  0.317887
c -0.605254  1.099116 -0.252589
d       NaN  2.845746 -1.435277

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
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 [49]: np.mean(df['one'])
Out[49]: -0.20175122316962471

In [50]: np.mean(df['one'].values)
Out[50]: nan

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

In [51]: series = Series(randn(500))

In [52]: series[20:500] = np.nan

In [53]: series[10:20]  = 5

In [54]: series.nunique()
Out[54]: 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 [55]: series = Series(randn(1000))

In [56]: series[::2] = np.nan

In [57]: series.describe()
Out[57]: 
count    500.000000
mean      -0.010874
std        0.978826
min       -3.308912
25%       -0.671361
50%       -0.017340
75%        0.690285
max        2.899619

In [58]: frame = DataFrame(randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])

In [59]: frame.ix[::2] = np.nan

In [60]: frame.describe()
Out[60]: 
                a           b           c           d           e
count  500.000000  500.000000  500.000000  500.000000  500.000000
mean    -0.037198    0.003715   -0.038170    0.020266   -0.052238
std      1.031409    1.035933    1.020972    0.930793    0.948294
min     -3.022166   -2.900464   -3.859013   -2.531818   -3.079421
25%     -0.726927   -0.694731   -0.690598   -0.628765   -0.739466
50%     -0.099480   -0.045956    0.029342    0.017272   -0.031573
75%      0.651600    0.672744    0.619422    0.665282    0.556025
max      2.974450    3.529657    3.072295    2.752934    2.360835

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

In [61]: s = Series(['a', 'a', 'b', 'b', 'a', 'a', np.nan, 'c', 'd', 'a'])

In [62]: s.describe()
Out[62]: 
count     9
unique    4
top       a
freq      5

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 [63]: s1 = Series(randn(5))

In [64]: s1
Out[64]: 
0    1.074954
1   -0.723240
2    0.768565
3   -0.419779
4    1.409733

In [65]: s1.idxmin(), s1.idxmax()
Out[65]: (1, 4)

In [66]: df1 = DataFrame(randn(5,3), columns=['A','B','C'])

In [67]: df1
Out[67]: 
          A         B         C
0  1.867787  0.398379 -0.367140
1  0.082227  0.664048 -0.887732
2  1.215313  0.083878 -0.398991
3  1.179368 -1.409235  0.090943
4 -0.278859 -1.348990 -0.191104

In [68]: df1.idxmin(axis=0)
Out[68]: 
A    4
B    3
C    1

In [69]: df1.idxmax(axis=1)
Out[69]: 
0    A
1    B
2    A
3    A
4    C

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

In [70]: df3 = DataFrame([2, 1, 1, 3, np.nan], columns=['A'], index=list('edcba'))

In [71]: df3
Out[71]: 
    A
e   2
d   1
c   1
b   3
a NaN

In [72]: df3['A'].idxmin()
Out[72]: 'd'

Value counts (histogramming)

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

In [74]: data
Out[74]: 
array([4, 6, 1, 3, 6, 3, 3, 0, 2, 6, 4, 0, 0, 6, 0, 5, 2, 6, 4, 1, 1, 0, 1,
       4, 6, 4, 6, 6, 4, 5, 4, 4, 4, 4, 4, 2, 3, 1, 3, 4, 6, 0, 1, 4, 3, 1,
       4, 4, 2, 3])

In [75]: s = Series(data)

In [76]: s.value_counts()
Out[76]: 
4    15
6     9
3     7
1     7
0     6
2     4
5     2

In [77]: value_counts(data)
Out[77]: 
4    15
6     9
3     7
1     7
0     6
2     4
5     2

Discretization and quantiling

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

In [78]: arr = np.random.randn(20)

In [79]: factor = cut(arr, 4)

In [80]: factor
Out[80]: 
Categorical: 
array([(-0.182, 0.488], (-0.854, -0.182], (0.488, 1.158], (-0.854, -0.182],
       (1.158, 1.828], (-0.182, 0.488], (-0.182, 0.488], (-0.854, -0.182],
       (0.488, 1.158], (-0.854, -0.182], (-0.182, 0.488], (0.488, 1.158],
       (1.158, 1.828], (-0.182, 0.488], (0.488, 1.158], (-0.854, -0.182],
       (1.158, 1.828], (0.488, 1.158], (-0.182, 0.488], (0.488, 1.158]], dtype=object)
Levels (4): Index([(-0.854, -0.182], (-0.182, 0.488], (0.488, 1.158],
                   (1.158, 1.828]], dtype=object)

In [81]: factor = cut(arr, [-5, -1, 0, 1, 5])

In [82]: factor
Out[82]: 
Categorical: 
array([(0, 1], (-1, 0], (0, 1], (-1, 0], (1, 5], (0, 1], (0, 1], (-1, 0],
       (0, 1], (-1, 0], (-1, 0], (0, 1], (1, 5], (0, 1], (0, 1], (-1, 0],
       (1, 5], (0, 1], (0, 1], (0, 1]], dtype=object)
Levels (4): Index([(-5, -1], (-1, 0], (0, 1], (1, 5]], dtype=object)

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

In [83]: arr = np.random.randn(30)

In [84]: factor = qcut(arr, [0, .25, .5, .75, 1])

In [85]: factor
Out[85]: 
Categorical: 
array([[-2.641, -0.57], (0.506, 1.875], [-2.641, -0.57], (-0.57, 0.0412],
       (0.0412, 0.506], (0.0412, 0.506], (-0.57, 0.0412], [-2.641, -0.57],
       (0.0412, 0.506], (0.0412, 0.506], [-2.641, -0.57], [-2.641, -0.57],
       (-0.57, 0.0412], (0.0412, 0.506], (0.0412, 0.506], [-2.641, -0.57],
       [-2.641, -0.57], (-0.57, 0.0412], (0.506, 1.875], (0.506, 1.875],
       (0.506, 1.875], (0.506, 1.875], (0.0412, 0.506], (-0.57, 0.0412],
       (0.506, 1.875], [-2.641, -0.57], (-0.57, 0.0412], (-0.57, 0.0412],
       (0.506, 1.875], (0.506, 1.875]], dtype=object)
Levels (4): Index([[-2.641, -0.57], (-0.57, 0.0412], (0.0412, 0.506],
                   (0.506, 1.875]], dtype=object)

In [86]: value_counts(factor)
Out[86]: 
(0.506, 1.875]     8
[-2.641, -0.57]    8
(0.0412, 0.506]    7
(-0.57, 0.0412]    7

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 [87]: df.apply(np.mean)
Out[87]: 
one     -0.201751
three    0.948582
two     -0.358819

In [88]: df.apply(np.mean, axis=1)
Out[88]: 
a   -0.040676
b    0.092688
c    0.014854
d    0.281971

In [89]: df.apply(lambda x: x.max() - x.min())
Out[89]: 
one      2.137073
three    1.439530
two      2.193867

In [90]: df.apply(np.cumsum)
Out[90]: 
        one     three       two
a  0.611941       NaN -0.693293
b -0.913191  0.792016  0.317887
c -0.605254  1.099116 -0.252589
d       NaN  2.845746 -1.435277

In [91]: df.apply(np.exp)
Out[91]: 
        one     three       two
a  1.844007       NaN  0.499927
b  0.217592  2.207843  2.748841
c  1.360615  1.359477  0.565256
d       NaN  5.735244  0.306454

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

In [93]: tsdf.apply(lambda x: x.index[x.dropna().argmax()])
Out[93]: 
A    2000-02-29 00:00:00
B    2001-09-02 00:00:00
C    2000-03-07 00:00:00

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 [94]: tsdf
Out[94]: 
                   A         B         C
2000-01-01  1.645644  0.855943  0.570849
2000-01-02 -0.936910  0.423672 -1.365350
2000-01-03 -0.151807 -0.564158 -1.921982
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  0.797360  0.713577 -0.648418
2000-01-09  1.389068 -1.311728  0.373535
2000-01-10 -0.608887  0.232901 -1.418048

In [95]: tsdf.apply(Series.interpolate)
Out[95]: 
                   A         B         C
2000-01-01  1.645644  0.855943  0.570849
2000-01-02 -0.936910  0.423672 -1.365350
2000-01-03 -0.151807 -0.564158 -1.921982
2000-01-04  0.038027 -0.308611 -1.667269
2000-01-05  0.227860 -0.053064 -1.412556
2000-01-06  0.417693  0.202483 -1.157844
2000-01-07  0.607526  0.458030 -0.903131
2000-01-08  0.797360  0.713577 -0.648418
2000-01-09  1.389068 -1.311728  0.373535
2000-01-10 -0.608887  0.232901 -1.418048

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 [96]: f = lambda x: len(str(x))

In [97]: df['one'].map(f)
Out[97]: 
a    14
b    13
c    14
d     3
Name: one

In [98]: df.applymap(f)
Out[98]: 
   one  three  two
a   14      3   15
b   13     14   13
c   14     14   15
d    3     13   14

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

In [100]: t = Series({'six' : 6., 'seven' : 7.})

In [101]: s
Out[101]: 
a      six
b    seven
c      six
d    seven
e      six

In [102]: s.map(t)
Out[102]: 
a    6
b    7
c    6
d    7
e    6

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

In [104]: s
Out[104]: 
a    0.570057
b    1.043702
c    1.299836
d   -1.815136
e    0.580098

In [105]: s.reindex(['e', 'b', 'f', 'd'])
Out[105]: 
e    0.580098
b    1.043702
f         NaN
d   -1.815136

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 [106]: df
Out[106]: 
        one     three       two
a  0.611941       NaN -0.693293
b -1.525132  0.792016  1.011179
c  0.307937  0.307100 -0.570476
d       NaN  1.746630 -1.182688

In [107]: df.reindex(index=['c', 'f', 'b'], columns=['three', 'two', 'one'])
Out[107]: 
      three       two       one
c  0.307100 -0.570476  0.307937
f       NaN       NaN       NaN
b  0.792016  1.011179 -1.525132

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

In [109]: rs
Out[109]: 
a    0.570057
b    1.043702
c    1.299836
d   -1.815136

In [110]: rs.index is df.index
Out[110]: 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 sprinking 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 [111]: df
Out[111]: 
        one     three       two
a  0.611941       NaN -0.693293
b -1.525132  0.792016  1.011179
c  0.307937  0.307100 -0.570476
d       NaN  1.746630 -1.182688

In [112]: df2
Out[112]: 
        one       two
a  0.813692 -0.609096
b -1.323380  1.095376
c  0.509688 -0.486279

In [113]: df.reindex_like(df2)
Out[113]: 
        one       two
a  0.611941 -0.693293
b -1.525132  1.011179
c  0.307937 -0.570476

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

In [115]: s1 = s[:4]

In [116]: s2 = s[1:]

In [117]: s1.align(s2)
Out[117]: 
(a   -0.161053
b    1.058664
c    0.692338
d   -0.765060
e         NaN,
 a         NaN
b    1.058664
c    0.692338
d   -0.765060
e    0.296593)

In [118]: s1.align(s2, join='inner')
Out[118]: 
(b    1.058664
c    0.692338
d   -0.765060,
 b    1.058664
c    0.692338
d   -0.765060)

In [119]: s1.align(s2, join='left')
Out[119]: 
(a   -0.161053
b    1.058664
c    0.692338
d   -0.765060,
 a         NaN
b    1.058664
c    0.692338
d   -0.765060)

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

In [120]: df.align(df2, join='inner')
Out[120]: 
(        one       two
a  0.611941 -0.693293
b -1.525132  1.011179
c  0.307937 -0.570476,
         one       two
a  0.813692 -0.609096
b -1.323380  1.095376
c  0.509688 -0.486279)

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

In [121]: df.align(df2, join='inner', axis=0)
Out[121]: 
(        one     three       two
a  0.611941       NaN -0.693293
b -1.525132  0.792016  1.011179
c  0.307937  0.307100 -0.570476,
         one       two
a  0.813692 -0.609096
b -1.323380  1.095376
c  0.509688 -0.486279)

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 [122]: df.align(df2.ix[0], axis=1)
Out[122]: 
(        one     three       two
a  0.611941       NaN -0.693293
b -1.525132  0.792016  1.011179
c  0.307937  0.307100 -0.570476
d       NaN  1.746630 -1.182688,
 one      0.813692
three         NaN
two     -0.609096
Name: a)

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

In [124]: ts = Series(randn(8), index=rng)

In [125]: ts2 = ts[[0, 3, 6]]

In [126]: ts
Out[126]: 
2000-01-03    0.137030
2000-01-04   -1.600891
2000-01-05    0.081669
2000-01-06   -0.336161
2000-01-07   -1.784500
2000-01-08   -0.752448
2000-01-09    1.036993
2000-01-10   -0.435820
Freq: D

In [127]: ts2
Out[127]: 
2000-01-03    0.137030
2000-01-06   -0.336161
2000-01-09    1.036993

In [128]: ts2.reindex(ts.index)
Out[128]: 
2000-01-03    0.137030
2000-01-04         NaN
2000-01-05         NaN
2000-01-06   -0.336161
2000-01-07         NaN
2000-01-08         NaN
2000-01-09    1.036993
2000-01-10         NaN
Freq: D

In [129]: ts2.reindex(ts.index, method='ffill')
Out[129]: 
2000-01-03    0.137030
2000-01-04    0.137030
2000-01-05    0.137030
2000-01-06   -0.336161
2000-01-07   -0.336161
2000-01-08   -0.336161
2000-01-09    1.036993
2000-01-10    1.036993
Freq: D

In [130]: ts2.reindex(ts.index, method='bfill')
Out[130]: 
2000-01-03    0.137030
2000-01-04   -0.336161
2000-01-05   -0.336161
2000-01-06   -0.336161
2000-01-07    1.036993
2000-01-08    1.036993
2000-01-09    1.036993
2000-01-10         NaN
Freq: D

Note the same result could have been achieved using fillna:

In [131]: ts2.reindex(ts.index).fillna(method='ffill')
Out[131]: 
2000-01-03    0.137030
2000-01-04    0.137030
2000-01-05    0.137030
2000-01-06   -0.336161
2000-01-07   -0.336161
2000-01-08   -0.336161
2000-01-09    1.036993
2000-01-10    1.036993
Freq: D

Note these methods generally assume that the indexes are sorted. They may be modified in the future to be a bit more flexible but as time series data is ordered most of the time anyway, this has not been a major priority.

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 [132]: df
Out[132]: 
        one     three       two
a  0.611941       NaN -0.693293
b -1.525132  0.792016  1.011179
c  0.307937  0.307100 -0.570476
d       NaN  1.746630 -1.182688

In [133]: df.drop(['a', 'd'], axis=0)
Out[133]: 
        one     three       two
b -1.525132  0.792016  1.011179
c  0.307937  0.307100 -0.570476

In [134]: df.drop(['one'], axis=1)
Out[134]: 
      three       two
a       NaN -0.693293
b  0.792016  1.011179
c  0.307100 -0.570476
d  1.746630 -1.182688

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

In [135]: df.reindex(df.index - ['a', 'd'])
Out[135]: 
        one     three       two
b -1.525132  0.792016  1.011179
c  0.307937  0.307100 -0.570476

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 [136]: s
Out[136]: 
a   -0.161053
b    1.058664
c    0.692338
d   -0.765060
e    0.296593

In [137]: s.rename(str.upper)
Out[137]: 
A   -0.161053
B    1.058664
C    0.692338
D   -0.765060
E    0.296593

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 [138]: df.rename(columns={'one' : 'foo', 'two' : 'bar'},
   .....:           index={'a' : 'apple', 'b' : 'banana', 'd' : 'durian'})
   .....:
Out[138]: 
             foo     three       bar
apple   0.611941       NaN -0.693293
banana -1.525132  0.792016  1.011179
c       0.307937  0.307100 -0.570476
durian       NaN  1.746630 -1.182688

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 [139]: 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 [140]: for item, frame in wp.iteritems():
   .....:     print item
   .....:     print frame
   .....:
Item1
                   A         B         C         D
2000-01-01 -0.644850  0.308486  0.550399  0.685347
2000-01-02  1.595523 -0.855795 -0.394423  0.416642
2000-01-03 -0.236063 -0.298447 -0.851487  0.345941
2000-01-04  0.563546  0.812151 -1.480826  1.153576
2000-01-05  2.415834  1.163810 -0.599922 -0.592326
Item2
                   A         B         C         D
2000-01-01  0.061291  0.670001  1.332734  1.470517
2000-01-02 -0.353711  0.836905 -0.538227 -0.850747
2000-01-03 -0.138751  0.216920  2.380042 -0.370159
2000-01-04 -0.211562  0.328985 -0.428296  1.367802
2000-01-05 -1.171254 -1.582820 -0.000831 -2.271295

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 [141]: for row_index, row in df2.iterrows():
   .....:     print '%s\n%s' % (row_index, row)
   .....:
a
one    0.813692
two   -0.609096
Name: a
b
one   -1.323380
two    1.095376
Name: b
c
one    0.509688
two   -0.486279
Name: c

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

In [142]: df2 = DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})

In [143]: print df2
   x  y
0  1  4
1  2  5
2  3  6

In [144]: print df2.T
   0  1  2
x  1  2  3
y  4  5  6

In [145]: df2_t = DataFrame(dict((idx,values) for idx, values in df2.iterrows()))

In [146]: print df2_t
   0  1  2
x  1  2  3
y  4  5  6

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 [147]: 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:

In [148]: s = Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])

In [149]: s.str.lower()
Out[149]: 
0       a
1       b
2       c
3    aaba
4    baca
5     NaN
6    caba
7     dog
8     cat

In [150]: s.str.upper()
Out[150]: 
0       A
1       B
2       C
3    AABA
4    BACA
5     NaN
6    CABA
7     DOG
8     CAT

In [151]: s.str.len()
Out[151]: 
0     1
1     1
2     1
3     4
4     4
5   NaN
6     4
7     3
8     3

Methods like split return a Series of lists:

In [152]: s2 = Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h'])

In [153]: s2.str.split('_')
Out[153]: 
0    [a, b, c]
1    [c, d, e]
2          NaN
3    [f, g, h]

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

In [154]: s2.str.split('_').str.get(1)
Out[154]: 
0      b
1      d
2    NaN
3      g

In [155]: s2.str.split('_').str[1]
Out[155]: 
0      b
1      d
2    NaN
3      g

Methods like replace and findall take regular expressions, too:

In [156]: s3 = Series(['A', 'B', 'C', 'Aaba', 'Baca',
   .....:             '', np.nan, 'CABA', 'dog', 'cat'])
   .....:

In [157]: s3
Out[157]: 
0       A
1       B
2       C
3    Aaba
4    Baca
5        
6     NaN
7    CABA
8     dog
9     cat

In [158]: s3.str.replace('^.a|dog', 'XX-XX ', case=False)
Out[158]: 
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

Methods like contains, startswith, and endswith takes an extra na arguement so missing values can be considered True or False:

In [159]: s4 = Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])

In [160]: s4.str.contains('A', na=False)
Out[160]: 
0     True
1    False
2    False
3     True
4    False
5      NaN
6     True
7    False
8    False
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
endswidth 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
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

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

In [162]: unsorted_df.sort_index()
Out[162]: 
      three       two       one
a       NaN -0.693293  0.611941
b  0.792016  1.011179 -1.525132
c  0.307100 -0.570476  0.307937
d  1.746630 -1.182688       NaN

In [163]: unsorted_df.sort_index(ascending=False)
Out[163]: 
      three       two       one
d  1.746630 -1.182688       NaN
c  0.307100 -0.570476  0.307937
b  0.792016  1.011179 -1.525132
a       NaN -0.693293  0.611941

In [164]: unsorted_df.sort_index(axis=1)
Out[164]: 
        one     three       two
a  0.611941       NaN -0.693293
d       NaN  1.746630 -1.182688
c  0.307937  0.307100 -0.570476
b -1.525132  0.792016  1.011179

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 [165]: df.sort_index(by='two')
Out[165]: 
        one     three       two
d       NaN  1.746630 -1.182688
a  0.611941       NaN -0.693293
c  0.307937  0.307100 -0.570476
b -1.525132  0.792016  1.011179

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

In [166]: df = DataFrame({'one':[2,1,1,1],'two':[1,3,2,4],'three':[5,4,3,2]})

In [167]: df[['one', 'two', 'three']].sort_index(by=['one','two'])
Out[167]: 
   one  two  three
2    1    2      3
1    1    3      4
3    1    4      2
0    2    1      5

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 [168]: s[2] = np.nan

In [169]: s.order()
Out[169]: 
0       A
3    Aaba
1       B
4    Baca
6    CABA
8     cat
7     dog
2     NaN
5     NaN

In [170]: s.order(na_last=False)
Out[170]: 
2     NaN
5     NaN
0       A
3    Aaba
1       B
4    Baca
6    CABA
8     cat
7     dog

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, type casting

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.

Data can be explicitly cast to a NumPy dtype by using the astype method or alternately passing the dtype keyword argument to the object constructor.

In [171]: df = DataFrame(np.arange(12).reshape((4, 3)))

In [172]: df[0].dtype
Out[172]: dtype('int64')

In [173]: df.astype(float)[0].dtype
Out[173]: dtype('float64')

In [174]: df = DataFrame(np.arange(12).reshape((4, 3)), dtype=float)

In [175]: df[0].dtype
Out[175]: dtype('float64')

Inferring better types for object columns

The convert_objects DataFrame method will attempt to convert dtype=object columns to a better NumPy dtype. Occasionally (after transposing multiple times, for example), a mixed-type DataFrame will end up with everything as dtype=object. This method attempts to fix that:

In [176]: df = DataFrame(randn(6, 3), columns=['a', 'b', 'c'])

In [177]: df['d'] = 'foo'

In [178]: df
Out[178]: 
          a         b         c    d
0 -2.120171 -0.145131  0.457632  foo
1  1.492276  1.504203 -0.060997  foo
2  0.072351  0.666014 -0.128367  foo
3  1.876152  1.284719 -0.663552  foo
4  1.378260  1.677771 -0.202693  foo
5 -0.239223  0.019239  0.861778  foo

In [179]: df = df.T.T

In [180]: df.dtypes
Out[180]: 
a    object
b    object
c    object
d    object

In [181]: converted = df.convert_objects()

In [182]: converted.dtypes
Out[182]: 
a    float64
b    float64
c    float64
d     object

Pickling and serialization

All pandas objects are equipped with save methods which use Python’s cPickle module to save data structures to disk using the pickle format.

In [183]: df
Out[183]: 
           a           b           c    d
0  -2.120171  -0.1451311   0.4576318  foo
1   1.492276    1.504203 -0.06099733  foo
2  0.0723512    0.666014  -0.1283673  foo
3   1.876152    1.284719  -0.6635525  foo
4    1.37826    1.677771  -0.2026927  foo
5 -0.2392234  0.01923872   0.8617779  foo

In [184]: df.save('foo.pickle')

The load function in the pandas namespace can be used to load any pickled pandas object (or any other pickled object) from file:

In [185]: load('foo.pickle')
Out[185]: 
           a           b           c    d
0  -2.120171  -0.1451311   0.4576318  foo
1   1.492276    1.504203 -0.06099733  foo
2  0.0723512    0.666014  -0.1283673  foo
3   1.876152    1.284719  -0.6635525  foo
4    1.37826    1.677771  -0.2026927  foo
5 -0.2392234  0.01923872   0.8617779  foo

There is also a save function which takes any object as its first argument:

In [186]: save(df, 'foo.pickle')

In [187]: load('foo.pickle')
Out[187]: 
           a           b           c    d
0  -2.120171  -0.1451311   0.4576318  foo
1   1.492276    1.504203 -0.06099733  foo
2  0.0723512    0.666014  -0.1283673  foo
3   1.876152    1.284719  -0.6635525  foo
4    1.37826    1.677771  -0.2026927  foo
5 -0.2392234  0.01923872   0.8617779  foo

Working with package options

Introduced in 0.10.0, pandas supports a new system for working with options. 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 [188]: import pandas as pd

In [189]: pd.options.display.max_rows
Out[189]: 100

In [190]: pd.options.display.max_rows = 999

In [191]: pd.options.display.max_rows
Out[191]: 999

There is also an API composed of 4 relavent 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.

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

In [192]: get_option("display.max_rows")
Out[192]: 999

In [193]: set_option("display.max_rows",101)

In [194]: get_option("display.max_rows")
Out[194]: 101

In [195]: set_option("max_r",102)

In [196]: get_option("display.max_rows")
Out[196]: 102

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

In [197]: try:
   .....:     get_option("display.max_")
   .....: except KeyError as e:
   .....:     print(e)
   .....:
  File "<ipython-input-197-7ccb78c48d28>", line 3
    except KeyError as e:
                         ^
IndentationError: unindent does not match any outer indentation level

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

The docstrings of all the functions document the available options, but you can also get a list of available options and their descriptions with describe_option. When called with no argument describe_option will print out descriptions for all available options.

In [198]: describe_option()
display.colheader_justify: 
: 'left'/'right'
        Controls the justification of column headers. used by DataFrameFormatter.
display.column_space: No description available.
display.date_dayfirst: 
: boolean
        When True, prints and parses dates with the day first, eg 20/01/2005
display.date_yearfirst: 
: boolean
        When True, prints and parses dates with the year first, eg 2005/01/20
display.encoding: 
: 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: 
: boolean
        Default False
        Whether to print out the full DataFrame repr for wide DataFrames
        across multiple lines.
        If False, the summary representation is shown.
display.float_format: 
: 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.line_width: 
: int
        Default 80
        When printing wide DataFrames, this is the width of each line.
display.max_columns: 
: 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.
        Either one, or both can be set to 0 (experimental). Pandas will figure
        out how big the terminal is and will not display more rows or/and
        columns that can fit on it.
display.max_colwidth: 
: 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: 
: int
        max_info_columns is used in DataFrame.info method to decide if
        per column information will be printed.
display.max_rows: 
: 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 an summary repr.
display.multi_sparse: 
: boolean
        Default True, "sparsify" MultiIndex display (don't display repeated
        elements in outer levels within groups)
display.notebook_repr_html: 
: boolean
        When True (default), IPython notebook will use html representation for
        pandas objects (if it is available).
display.pprint_nest_depth: 
: int
        Defaults to 3.
        Controls the number of nested levels to process when pretty-printing
display.precision: 
: int
        Floating point output precision (number of significant digits). This is
        only a suggestion
mode.sim_interactive: 
: boolean
        Default False
        Whether to simulate interactive mode for purposes of testing
mode.use_inf_as_null: 
: 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 [199]: describe_option("date")
display.date_dayfirst: 
: boolean
        When True, prints and parses dates with the day first, eg 20/01/2005
display.date_yearfirst: 
: 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 [200]: get_option("display.max_rows")
Out[200]: 100

In [201]: set_option("display.max_rows",999)

In [202]: get_option("display.max_rows")
Out[202]: 999

In [203]: reset_option("display.max_rows")

In [204]: get_option("display.max_rows")
Out[204]: 100

and you also set multiple options at once:

In [205]: reset_option("^display\.")

Console Output Formatting

Note: set_printoptions/ reset_printoptions are now deprecated (but functioning), and both, as well as set_eng_float_format, use the options API behind the scenes. The corresponding options now live under “print.XYZ”, and you can set them directly with get/set_option.

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

In [207]: df['a']/1.e3
Out[207]: 
0     -2.120m
1      1.492m
2     72.351u
3      1.876m
4      1.378m
5   -239.223u
Name: a

In [208]: df['a']/1.e6
Out[208]: 
0     -2.120u
1      1.492u
2     72.351n
3      1.876u
4      1.378u
5   -239.223n
Name: a

The set_printoptions function has a number of options for controlling how floating point numbers are formatted (using hte 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.

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