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
1.0
1
3.0
2
5.0
3
NaN
4
6.0
5
8.0
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]:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06'],
dtype='datetime64[ns]', freq='D')
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

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' : pd.Categorical(["test","train","test","train"]),
....:
'F' : 'foo' })
....:
In [11]: df2
Out[11]:
A
B
C
D
E
F
0
1.0 2013-01-02
1.0
3
test
foo
1
1.0 2013-01-02
1.0
3
train
foo
2
1.0 2013-01-02
1.0
3
test
foo
3
1.0 2013-01-02
1.0
3
train
foo

Having specific dtypes

In [12]: df2.dtypes
Out[12]:
A
float64
B
datetime64[ns]
C
float32
D
int32
E
category
F
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>
df2.A
df2.bool
df2.abs
df2.boxplot
df2.add
df2.C
df2.add_prefix
df2.clip
df2.add_suffix
df2.clip_lower
df2.align
df2.clip_upper
df2.all
df2.columns
df2.any
df2.combine
df2.append
df2.combine_first
df2.apply
df2.compound
df2.applymap
df2.consolidate
df2.as_blocks
df2.convert_objects
df2.asfreq
df2.copy
df2.as_matrix
df2.corr
df2.astype
df2.corrwith
df2.at
df2.count
df2.at_time
df2.cov
df2.axes
df2.cummax
df2.B
df2.cummin
df2.between_time
df2.cumprod
df2.bfill
df2.cumsum
df2.blocks
df2.D

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
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

Display the index, columns, and the underlying numpy data

In [16]: df.index
Out[16]:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06'],
dtype='datetime64[ns]', freq='D')
In [17]: df.columns
Out[17]: Index(['A', 'B', 'C', '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

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

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

Sorting by values

In [22]: df.sort_values(by='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

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 documentation Indexing and Selecting Data and MultiIndex / Advanced Indexing

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
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

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

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

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

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

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

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

For getting a value explicitly

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

Boolean Indexing

Using a single column’s values to select data.

In [39]: df[df.A > 0]
Out[39]:
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

Selecting values from a DataFrame where a boolean condition is met.

In [40]: df[df > 0]
Out[40]:
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

Using the isin() method for filtering:

In [41]: df2 = df.copy()
In [42]: df2['E'] = ['one', 'one','two','three','four','three']
In [43]: df2
Out[43]:
A
B
C
D
E
2013-01-01
0.469112 -0.282863 -1.509059 -1.135632
one
2013-01-02
1.212112 -0.173215
0.119209 -1.044236
one
2013-01-03 -0.861849 -2.104569 -0.494929
1.071804
two
2013-01-04
0.721555 -0.706771 -1.039575
0.271860
three
2013-01-05 -0.424972
0.567020
0.276232 -1.087401
four
2013-01-06 -0.673690
0.113648 -1.478427
0.524988
three
In [44]: df2[df2['E'].isin(['two','four'])]
Out[44]:
A
B
C
D
E
2013-01-03 -0.861849 -2.104569 -0.494929
1.071804
two
2013-01-05 -0.424972
0.567020
0.276232 -1.087401
four

Setting

Setting a new column automatically aligns the data by the indexes

In [45]: s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))
In [46]: s1
Out[46]:
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 [47]: df['F'] = s1

Setting values by label

In [48]: df.at[dates[0],'A'] = 0

Setting values by position

In [49]: df.iat[0,1] = 0

Setting by assigning with a numpy array

In [50]: df.loc[:,'D'] = np.array([5] * len(df))

The result of the prior setting operations

In [51]: df
Out[51]:
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
1.0
2013-01-03 -0.861849 -2.104569 -0.494929
5
2.0
2013-01-04
0.721555 -0.706771 -1.039575
5
3.0
2013-01-05 -0.424972
0.567020
0.276232
5
4.0
2013-01-06 -0.673690
0.113648 -1.478427
5
5.0

A where operation with setting.

In [52]: df2 = df.copy()
In [53]: df2[df2 > 0] = -df2
In [54]: df2
Out[54]:
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 -1.0
2013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.0
2013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.0
2013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.0
2013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.0

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 [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
In [56]: df1.loc[dates[0]:dates[1],'E'] = 1
In [57]: df1
Out[57]:
A
B
C
D
F
E
2013-01-01
0.000000
0.000000 -1.509059
5
NaN
1.0
2013-01-02
1.212112 -0.173215
0.119209
5
1.0
1.0
2013-01-03 -0.861849 -2.104569 -0.494929
5
2.0
NaN
2013-01-04
0.721555 -0.706771 -1.039575
5
3.0
NaN

To drop any rows that have missing data.

In [58]: df1.dropna(how='any')
Out[58]:
A
B
C
D
F
E
2013-01-02
1.212112 -0.173215
0.119209
5
1.0
1.0

Filling missing data

In [59]: df1.fillna(value=5)
Out[59]:
A
B
C
D
F
E
2013-01-01
0.000000
0.000000 -1.509059
5
5.0
1.0
2013-01-02
1.212112 -0.173215
0.119209
5
1.0
1.0
2013-01-03 -0.861849 -2.104569 -0.494929
5
2.0
5.0
2013-01-04
0.721555 -0.706771 -1.039575
5
3.0
5.0

To get the boolean mask where values are nan

In [60]: pd.isnull(df1)
Out[60]:
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

Operations

See the Basic section on Binary Ops

Stats

Operations in general exclude missing data.

Performing a descriptive statistic

In [61]: df.mean()
Out[61]:
A
-0.004474
B
-0.383981
C
-0.687758
D
5.000000
F
3.000000
dtype: float64

Same operation on the other axis

In [62]: df.mean(1)
Out[62]:
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 [63]: s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)
In [64]: s
Out[64]:
2013-01-01
NaN
2013-01-02
NaN
2013-01-03
1.0
2013-01-04
3.0
2013-01-05
5.0
2013-01-06
NaN
Freq: D, dtype: float64
In [65]: df.sub(s, axis='index')
Out[65]:
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
4.0
1.0
2013-01-04 -2.278445 -3.706771 -4.039575
2.0
0.0
2013-01-05 -5.424972 -4.432980 -4.723768
0.0 -1.0
2013-01-06
NaN
NaN
NaN
NaN
NaN

Apply

Applying functions to the data

In [66]: df.apply(np.cumsum)
Out[66]:
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
1.0
2013-01-03
0.350263 -2.277784 -1.884779
15
3.0
2013-01-04
1.071818 -2.984555 -2.924354
20
6.0
2013-01-05
0.646846 -2.417535 -2.648122
25
10.0
2013-01-06 -0.026844 -2.303886 -4.126549
30
15.0
In [67]: df.apply(lambda x: x.max() - x.min())
Out[67]:
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 [68]: s = pd.Series(np.random.randint(0, 7, size=10))
In [69]: s
Out[69]:
0
4
1
2
2
1
3
2
4
6
5
4
6
4
7
6
8
4
9
4
dtype: int64
In [70]: s.value_counts()
Out[70]:
4
5
6
2
2
2
1
1
dtype: int64

String Methods

Series is equipped with a set of string processing methods in the str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses regular expressions by default (and in some cases always uses them). See more at Vectorized String Methods.

In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
In [72]: s.str.lower()
Out[72]:
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 with concat():

In [73]: df = pd.DataFrame(np.random.randn(10, 4))
In [74]: df
Out[74]:
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
# break it into pieces
In [75]: pieces = [df[:3], df[3:7], df[7:]]
In [76]: pd.concat(pieces)
Out[76]:
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

Join

SQL style merges. See the Database style joining

In [77]: left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
In [78]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
In [79]: left
Out[79]:
key
lval
0
foo
1
1
foo
2
In [80]: right
Out[80]:
key
rval
0
foo
4
1
foo
5
In [81]: pd.merge(left, right, on='key')
Out[81]:
key
lval
rval
0
foo
1
4
1
foo
1
5
2
foo
2
4
3
foo
2
5

Another example that can be given is:

In [82]: left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})
In [83]: right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})
In [84]: left
Out[84]:
key
lval
0
foo
1
1
bar
2
In [85]: right
Out[85]:
key
rval
0
foo
4
1
bar
5
In [86]: pd.merge(left, right, on='key')
Out[86]:
key
lval
rval
0
foo
1
4
1
bar
2
5

Append

Append rows to a dataframe. See the Appending

In [87]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])
In [88]: df
Out[88]:
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
In [89]: s = df.iloc[3]
In [90]: df.append(s, ignore_index=True)
Out[90]:
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

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 [91]: 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 [92]: df
Out[92]:
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

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

In [93]: df.groupby('A').sum()
Out[93]:
C
D
A
bar -2.802588
2.42611
foo
3.146492 -0.63958

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

In [94]: df.groupby(['A','B']).sum()
Out[94]:
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

Reshaping

See the sections on Hierarchical Indexing and Reshaping.

Stack

In [95]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
....:
'foo', 'foo', 'qux', 'qux'],
....:
['one', 'two', 'one', 'two',
....:
'one', 'two', 'one', 'two']]))
....:
In [96]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
In [97]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
In [98]: df2 = df[:4]
In [99]: df2
Out[99]:
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

The stack() method “compresses” a level in the DataFrame’s columns.

In [100]: stacked = df2.stack()
In [101]: stacked
Out[101]:
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() isunstack(), which by default unstacks the last level:

In [102]: stacked.unstack()
Out[102]:
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
In [103]: stacked.unstack(1)
Out[103]:
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
In [104]: stacked.unstack(0)
Out[104]:
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

Pivot Tables

See the section on Pivot Tables.

In [105]: 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 [106]: df
Out[106]:
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

We can produce pivot tables from this data very easily:

In [107]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])
Out[107]:
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

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 [108]: rng = pd.date_range('1/1/2012', periods=100, freq='S')
In [109]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
In [110]: ts.resample('5Min').sum()
Out[110]:
2012-01-01
25083
Freq: 5T, dtype: int64

Time zone representation

In [111]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
In [112]: ts = pd.Series(np.random.randn(len(rng)), rng)
In [113]: ts
Out[113]:
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 [114]: ts_utc = ts.tz_localize('UTC')
In [115]: ts_utc
Out[115]:
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 [116]: ts_utc.tz_convert('US/Eastern')
Out[116]:
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 [117]: rng = pd.date_range('1/1/2012', periods=5, freq='M')
In [118]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [119]: ts
Out[119]:
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 [120]: ps = ts.to_period()
In [121]: ps
Out[121]:
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 [122]: ps.to_timestamp()
Out[122]:
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 [123]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
In [124]: ts = pd.Series(np.random.randn(len(prng)), prng)
In [125]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
In [126]: ts.head()
Out[126]:
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

Categoricals

Since version 0.15, pandas can include categorical data in a DataFrame. For full docs, see the categorical introduction and the API documentation.

In [127]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})

Convert the raw grades to a categorical data type.

In [128]: df["grade"] = df["raw_grade"].astype("category")
In [129]: df["grade"]
Out[129]:
0
a
1
b
2
b
3
a
4
a
5
e
Name: grade, dtype: category
Categories (3, object): [a, b, e]

Rename the categories to more meaningful names (assigning to Series.cat.categories is inplace!)

In [130]: df["grade"].cat.categories = ["very good", "good", "very bad"]

Reorder the categories and simultaneously add the missing categories (methods under Series .cat return a new Series per default).

In [131]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])
In [132]: df["grade"]
Out[132]:
0
very good
1
good
2
good
3
very good
4
very good
5
very bad
Name: grade, dtype: category
Categories (5, object): [very bad, bad, medium, good, very good]

Sorting is per order in the categories, not lexical order.

In [133]: df.sort_values(by="grade")
Out[133]:
id raw_grade
grade
5
6
e
very bad
1
2
b
good
2
3
b
good
0
1
a
very good
3
4
a
very good
4
5
a
very good

Grouping by a categorical column shows also empty categories.

In [134]: df.groupby("grade").size()
Out[134]:
grade
very bad
1
bad
0
medium
0
good
2
very good
3
dtype: int64

Plotting

Plotting docs.

In [135]: ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
In [136]: ts = ts.cumsum()
In [137]: ts.plot()
Out[137]: <matplotlib.axes._subplots.AxesSubplot at 0x11e3d3940>

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

In [138]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
.....:
columns=['A', 'B', 'C', 'D'])
.....:
In [139]: df = df.cumsum()
In [140]: plt.figure(); df.plot(); plt.legend(loc='best')
Out[140]: <matplotlib.legend.Legend at 0x1200fc7b8>

Getting Data In/Out

CSV

Writing to a csv file

In [141]: df.to_csv('foo.csv')

Reading from a csv file

In [142]: pd.read_csv('foo.csv')
Out[142]:
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
..
...
...
...
...
...
993
2002-09-20 -10.628548
-9.153563 -7.883146
28.313940
994
2002-09-21 -10.390377
-8.727491 -6.399645
30.914107
995
2002-09-22
-8.985362
-8.485624 -4.669462
31.367740
996
2002-09-23
-9.558560
-8.781216 -4.499815
30.518439
997
2002-09-24
-9.902058
-9.340490 -4.386639
30.105593
998
2002-09-25 -10.216020
-9.480682 -3.933802
29.758560
999
2002-09-26 -11.856774 -10.671012 -3.216025
29.369368
[1000 rows x 5 columns]

HDF5

Reading and writing to HDFStores

Writing to a HDF5 Store

In [143]: df.to_hdf('foo.h5','df')

Reading from a HDF5 Store

In [144]: pd.read_hdf('foo.h5','df')
Out[144]:
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
...
...
...
...
...
2002-09-20 -10.628548
-9.153563 -7.883146
28.313940
2002-09-21 -10.390377
-8.727491 -6.399645
30.914107
2002-09-22
-8.985362
-8.485624 -4.669462
31.367740
2002-09-23
-9.558560
-8.781216 -4.499815
30.518439
2002-09-24
-9.902058
-9.340490 -4.386639
30.105593
2002-09-25 -10.216020
-9.480682 -3.933802
29.758560
2002-09-26 -11.856774 -10.671012 -3.216025
29.369368
[1000 rows x 4 columns]

Excel

Reading and writing to MS Excel

Writing to an excel file

In [145]: df.to_excel('foo.xlsx', sheet_name='Sheet1')

Reading from an excel file

In [146]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])
Out[146]:
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
...
...
...
...
...
2002-09-20 -10.628548
-9.153563 -7.883146
28.313940
2002-09-21 -10.390377
-8.727491 -6.399645
30.914107
2002-09-22
-8.985362
-8.485624 -4.669462
31.367740
2002-09-23
-9.558560
-8.781216 -4.499815
30.518439
2002-09-24
-9.902058
-9.340490 -4.386639
30.105593
2002-09-25 -10.216020
-9.480682 -3.933802
29.758560
2002-09-26 -11.856774 -10.671012 -3.216025
29.369368
[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().