原文出处:http://pandas.pydata.org/pandas-docs/stable/10min.html

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.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.isna(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() is unstack(), 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

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 0x109464b70>

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 0x109a28860>

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().

See Comparisons for an explanation and what to do.

See Gotchas as well.

《10 minutes to pandas》(转)的更多相关文章

  1. 转《在浏览器中使用tensorflow.js进行人脸识别的JavaScript API》

    作者 | Vincent Mühle 编译 | 姗姗 出品 | 人工智能头条(公众号ID:AI_Thinker) [导读]随着深度学习方法的应用,浏览器调用人脸识别技术已经得到了更广泛的应用与提升.在 ...

  2. face-api.js:一个在浏览器中进行人脸识别的 JavaScript 接口

    Mark! 本文将为大家介绍一个建立在「tensorflow.js」内核上的 javascript API——「face-api.js」,它实现了三种卷积神经网络架构,用于完成人脸检测.识别和特征点检 ...

  3. TensorFlow.js之安装与核心概念

    TensorFlow.js是通过WebGL加速.基于浏览器的机器学习js框架.通过tensorflow.js,我们可以在浏览器中开发机器学习.运行现有的模型或者重新训练现有的模型. 一.安装     ...

  4. 在Java中直接调用js代码(转载)

    http://blog.csdn.net/xzyxuanyuan/article/details/8062887 JDK1.6版添加了新的ScriptEngine类,允许用户直接执行js代码. 在Ja ...

  5. 第十一章:WEB浏览器中的javascript

    客户端javascript涵盖在本系列的第二部分第10章,主要讲解javascript是如何在web浏览器中实现的,这些章节介绍了大量的脚本宿主对象,这些对象可以表示浏览器窗口.文档树的内容.这些章节 ...

  6. 在Java中直接调用js代码

    JDK1.6版添加了新的ScriptEngine类,允许用户直接执行js代码. 在Java中直接调用js代码 不能调用浏览器中定义的js函数,会抛出异常提示ReferenceError: “alert ...

  7. TensorFlow.js入门(一)一维向量的学习

    TensorFlow的介绍   TensorFlow是谷歌基于DistBelief进行研发的第二代人工智能学习系统,其命名来源于本身的运行原理.Tensor(张量)意味着N维数组,Flow(流)意味着 ...

  8. JavaScript权威指南--WEB浏览器中的javascript

    知识要点 1.客户端javascript window对象是所有客户端javascript特性和API的主要接入点.它表示web浏览器的一个窗口或窗体,并且可以用window表示来引用它.window ...

  9. 解决webkit浏览器中js方法中使用window.event提示未定义的问题

    这实际上是一个浏览器兼容性问题,根源百度中一大堆,简要说就是ie中event对象是全局变量,所以哪里都能使用到,但是webkit内核的浏览器中却不存在这个全局变量event,而是以一个隐式的局部变量的 ...

  10. JS Date当前时间:获取日期时间方法在各浏览器中的差异

    转自:http://www.feiesoft.com/00047/<script type="text/javascript"> // JS Date当前时间获取方法在 ...

随机推荐

  1. Codeforces 892 A.Greed

    A. Greed time limit per test 2 seconds memory limit per test 256 megabytes input standard input outp ...

  2. poj 2081 简单递推

    #include<stdio.h> #include<string.h> #define N 510000 int dp[N]; int f[10000000]; int ma ...

  3. 最长递增子序列 (LIS) Longest Increasing Subsequence

    问题描述: 有一个长为n的数列a0, a1,..., an-1.请求出这个序列中最长的上升子序列.请求出这个序列中最长的上升子序列. 上升子序列:对于任意i<j都满足ai<aj的子序列. ...

  4. 2016 Multi-University Training Contest 7 solutions BY SYSU

    Ants 首先求出每个点的最近点. 可以直接对所有点构造kd树,然后在kd树上查询除本身以外的最近点,因为对所有点都求一次,所以不用担心退化. 也可以用分治做,同样是O(NlogN)的复杂度. 方法是 ...

  5. 【BZOJ3295】动态逆序对(BIT套动态加点线段树)

    题意:对于序列A,它的逆序对数定义为满足i<j,且Ai>Aj的数对(i,j)的个数. 给1到n的一个排列,按照某种顺序依次删除m个元素,你的任务是在每次删除一个元素之前统计整个序列的逆序对 ...

  6. 把excel导入到mysql中

    方法很多,不过建议你先看看mysql的开发文档,里面写的很详细的,如果你懒得看,可以看下面的 1.有个软件PHP Excel Parser Pro v4.2可以 2.可将Excel存成csv格式.然后 ...

  7. Ubuntu 16.04安装双显卡驱动方法收集

    说明:不一定有效,要不断尝试. http://www.linuxwang.com/html/2150.html http://blog.csdn.net/feishicheng/article/det ...

  8. Java的条件判断

    以下内容引用自http://wiki.jikexueyuan.com/project/java/decision-making.html: 在 Java中有两种类型的条件判断语句,它们分别是: if语 ...

  9. java截屏简单例子

    原文:http://www.open-open.com/code/view/1444211411979 java截屏 * 运行后将当前屏幕截取,并最大化显示. * 拖拽鼠标,选择自己需要的部分. * ...

  10. PAT-PAT (Advanced Level) Practise 1001. A+B Format (20) 【二星级】

    题目链接:http://www.patest.cn/contests/pat-a-practise/1001 题面: 1001. A+B Format (20) Calculate a + b and ...