十分钟了解pandas

10 Minutes to Pandas

This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the Cookbook

Customarily, we import as follows

In [1]: import pandas as pd

In [2]: import numpy as np

In [3]: import matplotlib.pyplot as plt

Object Creation

See the Data Structure Intro section

Creating a Series by passing a list of values, letting pandas create a default integer index

In [4]: s = pd.Series([1,3,5,np.nan,6,8])

In [5]: s
Out[5]: 
0     1
1     3
2     5
3   NaN
4     6
5     8
dtype: float64

Creating a DataFrame by passing a numpy array, with a datetime index and labeled columns.

In [6]: dates = pd.date_range('20130101',periods=6)

In [7]: dates
Out[7]: 

[2013-01-01, ..., 2013-01-06]
Length: 6, Freq: D, Timezone: None

In [8]: df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))

In [9]: df
Out[9]: 
                   A         B         C         D
2013-01-01  0.469112 -0.282863 -1.509059 -1.135632
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804
2013-01-04  0.721555 -0.706771 -1.039575  0.271860
2013-01-05 -0.424972  0.567020  0.276232 -1.087401
2013-01-06 -0.673690  0.113648 -1.478427  0.524988

[6 rows x 4 columns]

Creating a DataFrame by passing a dict of objects that can be converted to series-like.

In [10]: df2 = pd.DataFrame({ 'A' : 1.,
   ....:                      'B' : pd.Timestamp('20130102'),
   ....:                      'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
   ....:                      'D' : np.array([3] * 4,dtype='int32'),
   ....:                      'E' : 'foo' })
   ....: 

In [11]: df2
Out[11]: 
   A          B  C  D    E
0  1 2013-01-02  1  3  foo
1  1 2013-01-02  1  3  foo
2  1 2013-01-02  1  3  foo
3  1 2013-01-02  1  3  foo

[4 rows x 5 columns]

Having specific dtypes

In [12]: df2.dtypes
Out[12]: 
A           float64
B    datetime64[ns]
C           float32
D             int32
E            object
dtype: object

If you’re using IPython, tab completion for column names (as well as public attributes) is automatically enabled. Here’s a subset of the attributes that will be completed:

In [13]: df2.<TAB>

As you can see, the columns A, B, C, and D are automatically tab completed. E is there as well; the rest of the attributes have been truncated for brevity.

Viewing Data¶

See the Basics section

See the top & bottom rows of the frame

In [14]: df.head()
Out[14]: 
                   A         B         C         D
2013-01-01  0.469112 -0.282863 -1.509059 -1.135632
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804
2013-01-04  0.721555 -0.706771 -1.039575  0.271860
2013-01-05 -0.424972  0.567020  0.276232 -1.087401

[5 rows x 4 columns]

In [15]: df.tail(3)
Out[15]: 
                   A         B         C         D
2013-01-04  0.721555 -0.706771 -1.039575  0.271860
2013-01-05 -0.424972  0.567020  0.276232 -1.087401
2013-01-06 -0.673690  0.113648 -1.478427  0.524988

[3 rows x 4 columns]

Display the index,columns, and the underlying numpy data

In [16]: df.index
Out[16]: 

[2013-01-01, ..., 2013-01-06]
Length: 6, Freq: D, Timezone: None

In [17]: df.columns
Out[17]: Index([u'A', u'B', u'C', u'D'], dtype='object')

In [18]: df.values
Out[18]: 
array([[ 0.4691, -0.2829, -1.5091, -1.1356],
       [ 1.2121, -0.1732,  0.1192, -1.0442],
       [-0.8618, -2.1046, -0.4949,  1.0718],
       [ 0.7216, -0.7068, -1.0396,  0.2719],
       [-0.425 ,  0.567 ,  0.2762, -1.0874],
       [-0.6737,  0.1136, -1.4784,  0.525 ]])

Describe shows a quick statistic summary of your data

In [19]: df.describe()
Out[19]: 
              A         B         C         D
count  6.000000  6.000000  6.000000  6.000000
mean   0.073711 -0.431125 -0.687758 -0.233103
std    0.843157  0.922818  0.779887  0.973118
min   -0.861849 -2.104569 -1.509059 -1.135632
25%   -0.611510 -0.600794 -1.368714 -1.076610
50%    0.022070 -0.228039 -0.767252 -0.386188
75%    0.658444  0.041933 -0.034326  0.461706
max    1.212112  0.567020  0.276232  1.071804

[8 rows x 4 columns]

Transposing your data

In [20]: df.T
Out[20]: 
   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06
A    0.469112    1.212112   -0.861849    0.721555   -0.424972   -0.673690
B   -0.282863   -0.173215   -2.104569   -0.706771    0.567020    0.113648
C   -1.509059    0.119209   -0.494929   -1.039575    0.276232   -1.478427
D   -1.135632   -1.044236    1.071804    0.271860   -1.087401    0.524988

[4 rows x 6 columns]

Sorting by an axis

In [21]: df.sort_index(axis=1, ascending=False)
Out[21]: 
                   D         C         B         A
2013-01-01 -1.135632 -1.509059 -0.282863  0.469112
2013-01-02 -1.044236  0.119209 -0.173215  1.212112
2013-01-03  1.071804 -0.494929 -2.104569 -0.861849
2013-01-04  0.271860 -1.039575 -0.706771  0.721555
2013-01-05 -1.087401  0.276232  0.567020 -0.424972
2013-01-06  0.524988 -1.478427  0.113648 -0.673690

[6 rows x 4 columns]

Sorting by values

In [22]: df.sort(columns='B')
Out[22]: 
                   A         B         C         D
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804
2013-01-04  0.721555 -0.706771 -1.039575  0.271860
2013-01-01  0.469112 -0.282863 -1.509059 -1.135632
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-06 -0.673690  0.113648 -1.478427  0.524988
2013-01-05 -0.424972  0.567020  0.276232 -1.087401

[6 rows x 4 columns]

Selection¶

Note

While standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc, .iloc and .ix.

See the Indexing section and below.

Getting¶

Selecting a single column, which yields a Series, equivalent to df.A

In [23]: df['A']
Out[23]: 
2013-01-01    0.469112
2013-01-02    1.212112
2013-01-03   -0.861849
2013-01-04    0.721555
2013-01-05   -0.424972
2013-01-06   -0.673690
Freq: D, Name: A, dtype: float64

Selecting via [], which slices the rows.

In [24]: df[0:3]
Out[24]: 
                   A         B         C         D
2013-01-01  0.469112 -0.282863 -1.509059 -1.135632
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804

[3 rows x 4 columns]

In [25]: df['20130102':'20130104']
Out[25]: 
                   A         B         C         D
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804
2013-01-04  0.721555 -0.706771 -1.039575  0.271860

[3 rows x 4 columns]

Selection by Label¶

See more in Selection by Label

For getting a cross section using a label

In [26]: df.loc[dates[0]]
Out[26]: 
A    0.469112
B   -0.282863
C   -1.509059
D   -1.135632
Name: 2013-01-01 00:00:00, dtype: float64

Selecting on a multi-axis by label

In [27]: df.loc[:,['A','B']]
Out[27]: 
                   A         B
2013-01-01  0.469112 -0.282863
2013-01-02  1.212112 -0.173215
2013-01-03 -0.861849 -2.104569
2013-01-04  0.721555 -0.706771
2013-01-05 -0.424972  0.567020
2013-01-06 -0.673690  0.113648

[6 rows x 2 columns]

Showing label slicing, both endpoints are included

In [28]: df.loc['20130102':'20130104',['A','B']]
Out[28]: 
                   A         B
2013-01-02  1.212112 -0.173215
2013-01-03 -0.861849 -2.104569
2013-01-04  0.721555 -0.706771

[3 rows x 2 columns]

Reduction in the dimensions of the returned object

In [29]: df.loc['20130102',['A','B']]
Out[29]: 
A    1.212112
B   -0.173215
Name: 2013-01-02 00:00:00, dtype: float64

For getting a scalar value

In [30]: df.loc[dates[0],'A']
Out[30]: 0.46911229990718628

For getting fast access to a scalar (equiv to the prior method)

In [31]: df.at[dates[0],'A']
Out[31]: 0.46911229990718628

Selection by Position¶

See more in Selection by Position

Select via the position of the passed integers

In [32]: df.iloc[3]
Out[32]: 
A    0.721555
B   -0.706771
C   -1.039575
D    0.271860
Name: 2013-01-04 00:00:00, dtype: float64

By integer slices, acting similar to numpy/python

In [33]: df.iloc[3:5,0:2]
Out[33]: 
                   A         B
2013-01-04  0.721555 -0.706771
2013-01-05 -0.424972  0.567020

[2 rows x 2 columns]

By lists of integer position locations, similar to the numpy/python style

In [34]: df.iloc[[1,2,4],[0,2]]
Out[34]: 
                   A         C
2013-01-02  1.212112  0.119209
2013-01-03 -0.861849 -0.494929
2013-01-05 -0.424972  0.276232

[3 rows x 2 columns]

For slicing rows explicitly

In [35]: df.iloc[1:3,:]
Out[35]: 
                   A         B         C         D
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804

[2 rows x 4 columns]

For slicing columns explicitly

In [36]: df.iloc[:,1:3]
Out[36]: 
                   B         C
2013-01-01 -0.282863 -1.509059
2013-01-02 -0.173215  0.119209
2013-01-03 -2.104569 -0.494929
2013-01-04 -0.706771 -1.039575
2013-01-05  0.567020  0.276232
2013-01-06  0.113648 -1.478427

[6 rows x 2 columns]

For getting a value explicity

In [37]: df.iloc[1,1]
Out[37]: -0.17321464905330858

For getting fast access to a scalar (equiv to the prior method)

In [38]: df.iat[1,1]
Out[38]: -0.17321464905330858

There is one signficant departure from standard python/numpy slicing semantics. python/numpy allow slicing past the end of an array without an associated error.

# these are allowed in python/numpy.
In [39]: x = list('abcdef')

In [40]: x[4:10]
Out[40]: ['e', 'f']

In [41]: x[8:10]
Out[41]: []

Pandas will detect this and raise IndexError, rather than return an empty structure.

>>> df.iloc[:,8:10]
IndexError: out-of-bounds on slice (end)

Boolean Indexing¶

Using a single column’s values to select data.

In [42]: df[df.A > 0]
Out[42]: 
                   A         B         C         D
2013-01-01  0.469112 -0.282863 -1.509059 -1.135632
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-04  0.721555 -0.706771 -1.039575  0.271860

[3 rows x 4 columns]

A where operation for getting.

In [43]: df[df > 0]
Out[43]: 
                   A         B         C         D
2013-01-01  0.469112       NaN       NaN       NaN
2013-01-02  1.212112       NaN  0.119209       NaN
2013-01-03       NaN       NaN       NaN  1.071804
2013-01-04  0.721555       NaN       NaN  0.271860
2013-01-05       NaN  0.567020  0.276232       NaN
2013-01-06       NaN  0.113648       NaN  0.524988

[6 rows x 4 columns]

Setting¶

Setting a new column automatically aligns the data by the indexes

In [44]: s1 = pd.Series([1,2,3,4,5,6],index=pd.date_range('20130102',periods=6))

In [45]: s1
Out[45]: 
2013-01-02    1
2013-01-03    2
2013-01-04    3
2013-01-05    4
2013-01-06    5
2013-01-07    6
Freq: D, dtype: int64

In [46]: df['F'] = s1

Setting values by label

In [47]: df.at[dates[0],'A'] = 0

Setting values by position

In [48]: df.iat[0,1] = 0

Setting by assigning with a numpy array

In [49]: df.loc[:,'D'] = np.array([5] * len(df))

The result of the prior setting operations

In [50]: df
Out[50]: 
                   A         B         C  D   F
2013-01-01  0.000000  0.000000 -1.509059  5 NaN
2013-01-02  1.212112 -0.173215  0.119209  5   1
2013-01-03 -0.861849 -2.104569 -0.494929  5   2
2013-01-04  0.721555 -0.706771 -1.039575  5   3
2013-01-05 -0.424972  0.567020  0.276232  5   4
2013-01-06 -0.673690  0.113648 -1.478427  5   5

[6 rows x 5 columns]

A where operation with setting.

In [51]: df2 = df.copy()

In [52]: df2[df2 > 0] = -df2

In [53]: df2
Out[53]: 
                   A         B         C  D   F
2013-01-01  0.000000  0.000000 -1.509059 -5 NaN
2013-01-02 -1.212112 -0.173215 -0.119209 -5  -1
2013-01-03 -0.861849 -2.104569 -0.494929 -5  -2
2013-01-04 -0.721555 -0.706771 -1.039575 -5  -3
2013-01-05 -0.424972 -0.567020 -0.276232 -5  -4
2013-01-06 -0.673690 -0.113648 -1.478427 -5  -5

[6 rows x 5 columns]

Missing Data¶

Pandas primarily uses the value np.nan to represent missing data. It is by default not included in computations. See the Missing Data section

Reindexing allows you to change/add/delete the index on a specified axis. This returns a copy of the data.

In [54]: df1 = df.reindex(index=dates[0:4],columns=list(df.columns) + ['E'])

In [55]: df1.loc[dates[0]:dates[1],'E'] = 1

In [56]: df1
Out[56]: 
                   A         B         C  D   F   E
2013-01-01  0.000000  0.000000 -1.509059  5 NaN   1
2013-01-02  1.212112 -0.173215  0.119209  5   1   1
2013-01-03 -0.861849 -2.104569 -0.494929  5   2 NaN
2013-01-04  0.721555 -0.706771 -1.039575  5   3 NaN

[4 rows x 6 columns]

To drop any rows that have missing data.

In [57]: df1.dropna(how='any')
Out[57]: 
                   A         B         C  D  F  E
2013-01-02  1.212112 -0.173215  0.119209  5  1  1

[1 rows x 6 columns]

Filling missing data

In [58]: df1.fillna(value=5)
Out[58]: 
                   A         B         C  D  F  E
2013-01-01  0.000000  0.000000 -1.509059  5  5  1
2013-01-02  1.212112 -0.173215  0.119209  5  1  1
2013-01-03 -0.861849 -2.104569 -0.494929  5  2  5
2013-01-04  0.721555 -0.706771 -1.039575  5  3  5

[4 rows x 6 columns]

To get the boolean mask where values are nan

In [59]: pd.isnull(df1)
Out[59]: 
                A      B      C      D      F      E
2013-01-01  False  False  False  False   True  False
2013-01-02  False  False  False  False  False  False
2013-01-03  False  False  False  False  False   True
2013-01-04  False  False  False  False  False   True

[4 rows x 6 columns]

Operations¶

See the Basic section on Binary Ops

Stats¶

Operations in general exclude missing data.

Performing a descriptive statistic

In [60]: df.mean()
Out[60]: 
A   -0.004474
B   -0.383981
C   -0.687758
D    5.000000
F    3.000000
dtype: float64

Same operation on the other axis

In [61]: df.mean(1)
Out[61]: 
2013-01-01    0.872735
2013-01-02    1.431621
2013-01-03    0.707731
2013-01-04    1.395042
2013-01-05    1.883656
2013-01-06    1.592306
Freq: D, dtype: float64

Operating with objects that have different dimensionality and need alignment. In addition, pandas automatically broadcasts along the specified dimension.

In [62]: s = pd.Series([1,3,5,np.nan,6,8],index=dates).shift(2)

In [63]: s
Out[63]: 
2013-01-01   NaN
2013-01-02   NaN
2013-01-03     1
2013-01-04     3
2013-01-05     5
2013-01-06   NaN
Freq: D, dtype: float64

In [64]: df.sub(s,axis='index')
Out[64]: 
                   A         B         C   D   F
2013-01-01       NaN       NaN       NaN NaN NaN
2013-01-02       NaN       NaN       NaN NaN NaN
2013-01-03 -1.861849 -3.104569 -1.494929   4   1
2013-01-04 -2.278445 -3.706771 -4.039575   2   0
2013-01-05 -5.424972 -4.432980 -4.723768   0  -1
2013-01-06       NaN       NaN       NaN NaN NaN

[6 rows x 5 columns]

Apply¶

Applying functions to the data

In [65]: df.apply(np.cumsum)
Out[65]: 
                   A         B         C   D   F
2013-01-01  0.000000  0.000000 -1.509059   5 NaN
2013-01-02  1.212112 -0.173215 -1.389850  10   1
2013-01-03  0.350263 -2.277784 -1.884779  15   3
2013-01-04  1.071818 -2.984555 -2.924354  20   6
2013-01-05  0.646846 -2.417535 -2.648122  25  10
2013-01-06 -0.026844 -2.303886 -4.126549  30  15

[6 rows x 5 columns]

In [66]: df.apply(lambda x: x.max() - x.min())
Out[66]: 
A    2.073961
B    2.671590
C    1.785291
D    0.000000
F    4.000000
dtype: float64

Histogramming¶

See more at Histogramming and Discretization

In [67]: s = pd.Series(np.random.randint(0,7,size=10))

In [68]: s
Out[68]: 
0    4
1    2
2    1
3    2
4    6
5    4
6    4
7    6
8    4
9    4
dtype: int64

In [69]: s.value_counts()
Out[69]: 
4    5
6    2
2    2
1    1
dtype: int64

String Methods¶

See more at Vectorized String Methods

In [70]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])

In [71]: s.str.lower()
Out[71]: 
0       a
1       b
2       c
3    aaba
4    baca
5     NaN
6    caba
7     dog
8     cat
dtype: object

Merge¶

Concat¶

Pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.

See the Merging section

Concatenating pandas objects together

In [72]: df = pd.DataFrame(np.random.randn(10, 4))

In [73]: df
Out[73]: 
          0         1         2         3
0 -0.548702  1.467327 -1.015962 -0.483075
1  1.637550 -1.217659 -0.291519 -1.745505
2 -0.263952  0.991460 -0.919069  0.266046
3 -0.709661  1.669052  1.037882 -1.705775
4 -0.919854 -0.042379  1.247642 -0.009920
5  0.290213  0.495767  0.362949  1.548106
6 -1.131345 -0.089329  0.337863 -0.945867
7 -0.932132  1.956030  0.017587 -0.016692
8 -0.575247  0.254161 -1.143704  0.215897
9  1.193555 -0.077118 -0.408530 -0.862495

[10 rows x 4 columns]

# break it into pieces
In [74]: pieces = [df[:3], df[3:7], df[7:]]

In [75]: pd.concat(pieces)
Out[75]: 
          0         1         2         3
0 -0.548702  1.467327 -1.015962 -0.483075
1  1.637550 -1.217659 -0.291519 -1.745505
2 -0.263952  0.991460 -0.919069  0.266046
3 -0.709661  1.669052  1.037882 -1.705775
4 -0.919854 -0.042379  1.247642 -0.009920
5  0.290213  0.495767  0.362949  1.548106
6 -1.131345 -0.089329  0.337863 -0.945867
7 -0.932132  1.956030  0.017587 -0.016692
8 -0.575247  0.254161 -1.143704  0.215897
9  1.193555 -0.077118 -0.408530 -0.862495

[10 rows x 4 columns]

Join¶

SQL style merges. See the Database style joining

In [76]: left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})

In [77]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})

In [78]: left
Out[78]: 
   key  lval
0  foo     1
1  foo     2

[2 rows x 2 columns]

In [79]: right
Out[79]: 
   key  rval
0  foo     4
1  foo     5

[2 rows x 2 columns]

In [80]: pd.merge(left, right, on='key')
Out[80]: 
   key  lval  rval
0  foo     1     4
1  foo     1     5
2  foo     2     4
3  foo     2     5

[4 rows x 3 columns]

Append¶

Append rows to a dataframe. See the Appending

In [81]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])

In [82]: df
Out[82]: 
          A         B         C         D
0  1.346061  1.511763  1.627081 -0.990582
1 -0.441652  1.211526  0.268520  0.024580
2 -1.577585  0.396823 -0.105381 -0.532532
3  1.453749  1.208843 -0.080952 -0.264610
4 -0.727965 -0.589346  0.339969 -0.693205
5 -0.339355  0.593616  0.884345  1.591431
6  0.141809  0.220390  0.435589  0.192451
7 -0.096701  0.803351  1.715071 -0.708758

[8 rows x 4 columns]

In [83]: s = df.iloc[3]

In [84]: df.append(s, ignore_index=True)
Out[84]: 
          A         B         C         D
0  1.346061  1.511763  1.627081 -0.990582
1 -0.441652  1.211526  0.268520  0.024580
2 -1.577585  0.396823 -0.105381 -0.532532
3  1.453749  1.208843 -0.080952 -0.264610
4 -0.727965 -0.589346  0.339969 -0.693205
5 -0.339355  0.593616  0.884345  1.591431
6  0.141809  0.220390  0.435589  0.192451
7 -0.096701  0.803351  1.715071 -0.708758
8  1.453749  1.208843 -0.080952 -0.264610

[9 rows x 4 columns]

Grouping¶

By “group by” we are referring to a process involving one or more of the following steps

• Splitting the data into groups based on some criteria
• Applying a function to each group independently
• Combining the results into a data structure

See the Grouping section

In [85]: df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
   ....:                          'foo', 'bar', 'foo', 'foo'],
   ....:                    'B' : ['one', 'one', 'two', 'three',
   ....:                          'two', 'two', 'one', 'three'],
   ....:                    'C' : np.random.randn(8),
   ....:                    'D' : np.random.randn(8)})
   ....: 

In [86]: df
Out[86]: 
     A      B         C         D
0  foo    one -1.202872 -0.055224
1  bar    one -1.814470  2.395985
2  foo    two  1.018601  1.552825
3  bar  three -0.595447  0.166599
4  foo    two  1.395433  0.047609
5  bar    two -0.392670 -0.136473
6  foo    one  0.007207 -0.561757
7  foo  three  1.928123 -1.623033

[8 rows x 4 columns]

Grouping and then applying a function sum to the resulting groups.

In [87]: df.groupby('A').sum()
Out[87]: 
            C        D
A                     
bar -2.802588  2.42611
foo  3.146492 -0.63958

[2 rows x 2 columns]

Grouping by multiple columns forms a hierarchical index, which we then apply the function.

In [88]: df.groupby(['A','B']).sum()
Out[88]: 
                  C         D
A   B                        
bar one   -1.814470  2.395985
    three -0.595447  0.166599
    two   -0.392670 -0.136473
foo one   -1.195665 -0.616981
    three  1.928123 -1.623033
    two    2.414034  1.600434

[6 rows x 2 columns]

Reshaping¶

See the section on Hierarchical Indexing and see the section on Reshaping).

Stack¶

In [89]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
   ....:                      'foo', 'foo', 'qux', 'qux'],
   ....:                     ['one', 'two', 'one', 'two',
   ....:                      'one', 'two', 'one', 'two']]))
   ....: 

In [90]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])

In [91]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])

In [92]: df2 = df[:4]

In [93]: df2
Out[93]: 
                     A         B
first second                    
bar   one     0.029399 -0.542108
      two     0.282696 -0.087302
baz   one    -1.575170  1.771208
      two     0.816482  1.100230

[4 rows x 2 columns]

The stack function “compresses” a level in the DataFrame’s columns.

In [94]: stacked = df2.stack()

In [95]: stacked
Out[95]: 
first  second   
bar    one     A    0.029399
               B   -0.542108
       two     A    0.282696
               B   -0.087302
baz    one     A   -1.575170
               B    1.771208
       two     A    0.816482
               B    1.100230
dtype: float64

With a “stacked” DataFrame or Series (having a MultiIndex as the index), the inverse operation of stack is unstack, which by default unstacks the last level:

In [96]: stacked.unstack()
Out[96]: 
                     A         B
first second                    
bar   one     0.029399 -0.542108
      two     0.282696 -0.087302
baz   one    -1.575170  1.771208
      two     0.816482  1.100230

[4 rows x 2 columns]

In [97]: stacked.unstack(1)
Out[97]: 
second        one       two
first                      
bar   A  0.029399  0.282696
      B -0.542108 -0.087302
baz   A -1.575170  0.816482
      B  1.771208  1.100230

[4 rows x 2 columns]

In [98]: stacked.unstack(0)
Out[98]: 
first          bar       baz
second                      
one    A  0.029399 -1.575170
       B -0.542108  1.771208
two    A  0.282696  0.816482
       B -0.087302  1.100230

[4 rows x 2 columns]

Pivot Tables¶

See the section on Pivot Tables.

In [99]: df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,
   ....:                    'B' : ['A', 'B', 'C'] * 4,
   ....:                    'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
   ....:                    'D' : np.random.randn(12),
   ....:                    'E' : np.random.randn(12)})
   ....: 

In [100]: df
Out[100]: 
        A  B    C         D         E
0     one  A  foo  1.418757 -0.179666
1     one  B  foo -1.879024  1.291836
2     two  C  foo  0.536826 -0.009614
3   three  A  bar  1.006160  0.392149
4     one  B  bar -0.029716  0.264599
5     one  C  bar -1.146178 -0.057409
6     two  A  foo  0.100900 -1.425638
7   three  B  foo -1.035018  1.024098
8     one  C  foo  0.314665 -0.106062
9     one  A  bar -0.773723  1.824375
10    two  B  bar -1.170653  0.595974
11  three  C  bar  0.648740  1.167115

[12 rows x 5 columns]

We can produce pivot tables from this data very easily:

In [101]: pd.pivot_table(df, values='D', rows=['A', 'B'], cols=['C'])
Out[101]: 
C             bar       foo
A     B                    
one   A -0.773723  1.418757
      B -0.029716 -1.879024
      C -1.146178  0.314665
three A  1.006160       NaN
      B       NaN -1.035018
      C  0.648740       NaN
two   A       NaN  0.100900
      B -1.170653       NaN
      C       NaN  0.536826

[9 rows x 2 columns]

Time Series¶

Pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications. See the Time Series section

In [102]: rng = pd.date_range('1/1/2012', periods=100, freq='S')

In [103]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)

In [104]: ts.resample('5Min', how='sum')
Out[104]: 
2012-01-01    25083
Freq: 5T, dtype: int64

Time zone representation

In [105]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')

In [106]: ts = pd.Series(np.random.randn(len(rng)), rng)

In [107]: ts
Out[107]: 
2012-03-06    0.464000
2012-03-07    0.227371
2012-03-08   -0.496922
2012-03-09    0.306389
2012-03-10   -2.290613
Freq: D, dtype: float64

In [108]: ts_utc = ts.tz_localize('UTC')

In [109]: ts_utc
Out[109]: 
2012-03-06 00:00:00+00:00    0.464000
2012-03-07 00:00:00+00:00    0.227371
2012-03-08 00:00:00+00:00   -0.496922
2012-03-09 00:00:00+00:00    0.306389
2012-03-10 00:00:00+00:00   -2.290613
Freq: D, dtype: float64

Convert to another time zone

In [110]: ts_utc.tz_convert('US/Eastern')
Out[110]: 
2012-03-05 19:00:00-05:00    0.464000
2012-03-06 19:00:00-05:00    0.227371
2012-03-07 19:00:00-05:00   -0.496922
2012-03-08 19:00:00-05:00    0.306389
2012-03-09 19:00:00-05:00   -2.290613
Freq: D, dtype: float64

Converting between time span representations

In [111]: rng = pd.date_range('1/1/2012', periods=5, freq='M')

In [112]: ts = pd.Series(np.random.randn(len(rng)), index=rng)

In [113]: ts
Out[113]: 
2012-01-31   -1.134623
2012-02-29   -1.561819
2012-03-31   -0.260838
2012-04-30    0.281957
2012-05-31    1.523962
Freq: M, dtype: float64

In [114]: ps = ts.to_period()

In [115]: ps
Out[115]: 
2012-01   -1.134623
2012-02   -1.561819
2012-03   -0.260838
2012-04    0.281957
2012-05    1.523962
Freq: M, dtype: float64

In [116]: ps.to_timestamp()
Out[116]: 
2012-01-01   -1.134623
2012-02-01   -1.561819
2012-03-01   -0.260838
2012-04-01    0.281957
2012-05-01    1.523962
Freq: MS, dtype: float64

Converting between period and timestamp enables some convenient arithmetic functions to be used. In the following example, we convert a quarterly frequency with year ending in November to 9am of the end of the month following the quarter end:

In [117]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')

In [118]: ts = pd.Series(np.random.randn(len(prng)), prng)

In [119]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9

In [120]: ts.head()
Out[120]: 
1990-03-01 09:00   -0.902937
1990-06-01 09:00    0.068159
1990-09-01 09:00   -0.057873
1990-12-01 09:00   -0.368204
1991-03-01 09:00   -1.144073
Freq: H, dtype: float64

Plotting¶

Plotting docs.

In [121]: ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))

In [122]: ts = ts.cumsum()

In [123]: ts.plot()
Out[123]: <matplotlib.axes.AxesSubplot at 0x59eb090>

On DataFrame, plot is a convenience to plot all of the columns with labels:

In [124]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
   .....:                   columns=['A', 'B', 'C', 'D'])
   .....: 

In [125]: df = df.cumsum()

In [126]: plt.figure(); df.plot(); plt.legend(loc='best')
Out[126]: <matplotlib.legend.Legend at 0x5df81d0>

Getting Data In/Out¶

CSV¶

Writing to a csv file

In [127]: df.to_csv('foo.csv')

Reading from a csv file

In [128]: pd.read_csv('foo.csv')
Out[128]: 
    Unnamed: 0         A         B         C         D
0   2000-01-01  0.266457 -0.399641 -0.219582  1.186860
1   2000-01-02 -1.170732 -0.345873  1.653061 -0.282953
2   2000-01-03 -1.734933  0.530468  2.060811 -0.515536
3   2000-01-04 -1.555121  1.452620  0.239859 -1.156896
4   2000-01-05  0.578117  0.511371  0.103552 -2.428202
5   2000-01-06  0.478344  0.449933 -0.741620 -1.962409
6   2000-01-07  1.235339 -0.091757 -1.543861 -1.084753
7   2000-01-08 -1.318492  0.003142 -3.863379 -0.791151
8   2000-01-09 -1.552842  1.292518 -4.772843 -0.471664
9   2000-01-10 -1.621025  0.074253 -5.866093 -0.162509
10  2000-01-11 -2.418239 -0.640980 -5.895733 -0.362802
11  2000-01-12 -3.350633 -0.358341 -5.917620 -0.444849
12  2000-01-13 -3.268737 -0.795976 -6.240176  0.497327
13  2000-01-14 -2.786138 -1.017654 -4.260442  0.631441
14  2000-01-15 -4.261365 -0.721180 -3.918269 -0.118960
           ...       ...       ...       ...       ...

[1000 rows x 5 columns]

HDF5¶

Reading and writing to HDFStores

Writing to a HDF5 Store

In [129]: df.to_hdf('foo.h5','df')

Reading from a HDF5 Store

In [130]: pd.read_hdf('foo.h5','df')
Out[130]: 
                   A         B         C         D
2000-01-01  0.266457 -0.399641 -0.219582  1.186860
2000-01-02 -1.170732 -0.345873  1.653061 -0.282953
2000-01-03 -1.734933  0.530468  2.060811 -0.515536
2000-01-04 -1.555121  1.452620  0.239859 -1.156896
2000-01-05  0.578117  0.511371  0.103552 -2.428202
2000-01-06  0.478344  0.449933 -0.741620 -1.962409
2000-01-07  1.235339 -0.091757 -1.543861 -1.084753
2000-01-08 -1.318492  0.003142 -3.863379 -0.791151
2000-01-09 -1.552842  1.292518 -4.772843 -0.471664
2000-01-10 -1.621025  0.074253 -5.866093 -0.162509
2000-01-11 -2.418239 -0.640980 -5.895733 -0.362802
2000-01-12 -3.350633 -0.358341 -5.917620 -0.444849
2000-01-13 -3.268737 -0.795976 -6.240176  0.497327
2000-01-14 -2.786138 -1.017654 -4.260442  0.631441
2000-01-15 -4.261365 -0.721180 -3.918269 -0.118960
                 ...       ...       ...       ...

[1000 rows x 4 columns]

Excel¶

Reading and writing to MS Excel

Writing to an excel file

In [131]: df.to_excel('foo.xlsx', sheet_name='Sheet1')

Reading from an excel file

In [132]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])
Out[132]: 
                   A         B         C         D
2000-01-01  0.266457 -0.399641 -0.219582  1.186860
2000-01-02 -1.170732 -0.345873  1.653061 -0.282953
2000-01-03 -1.734933  0.530468  2.060811 -0.515536
2000-01-04 -1.555121  1.452620  0.239859 -1.156896
2000-01-05  0.578117  0.511371  0.103552 -2.428202
2000-01-06  0.478344  0.449933 -0.741620 -1.962409
2000-01-07  1.235339 -0.091757 -1.543861 -1.084753
2000-01-08 -1.318492  0.003142 -3.863379 -0.791151
2000-01-09 -1.552842  1.292518 -4.772843 -0.471664
2000-01-10 -1.621025  0.074253 -5.866093 -0.162509
2000-01-11 -2.418239 -0.640980 -5.895733 -0.362802
2000-01-12 -3.350633 -0.358341 -5.917620 -0.444849
2000-01-13 -3.268737 -0.795976 -6.240176  0.497327
2000-01-14 -2.786138 -1.017654 -4.260442  0.631441
2000-01-15 -4.261365 -0.721180 -3.918269 -0.118960
                 ...       ...       ...       ...

[1000 rows x 4 columns]

Gotchas¶

If you are trying an operation and you see an exception like:

>>> if pd.Series([False, True, False]):
    print("I was true")
Traceback
    ...
ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().

See Comparisons for an explanation and what to do.

See Gotchas as well.