python pandas10分钟入门
This is a short introduction to pandas, geared mainly for new users.
Customarily, we import as follows
In : import pandas as pd
In : import numpy as np
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 : s = pd.Series([1,3,5,np.nan,6,8])
In : s
Out:
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 : dates = pd.date_range('20130101',periods=6)
In : dates
Out:
<class 'pandas.tseries.index.DatetimeIndex'>
Length: 6, Freq: D, Timezone: None
In : df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))
In : df
Out:
A B C D
2013-01-010.469112 -0.282863 -1.509059 -1.135632
2013-01-021.212112 -0.1732150.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.4949291.071804
2013-01-040.721555 -0.706771 -1.0395750.271860
2013-01-05 -0.4249720.5670200.276232 -1.087401
2013-01-06 -0.6736900.113648 -1.4784270.524988
Creating a DataFrame by passing a dict of objects that can be converted to series-like.
In : df2 = pd.DataFrame({ 'A' : 1.,
...: 'B' : pd.Timestamp('20130102'),
...: 'C' : pd.Series(1,index=range(4),dtype='float32'),
...: 'D' : np.array([3] * 4,dtype='int32'),
...: 'E' : 'foo' })
...:
In : df2
Out:
A BCD E
01 2013-01-02 00:00:0013foo
11 2013-01-02 00:00:0013foo
21 2013-01-02 00:00:0013foo
31 2013-01-02 00:00:0013foo
Having specific dtypes
In : df2.dtypes
Out:
A float64
B datetime64
C float32
D int32
E object
dtype: object
Viewing Data
See the Basics section
See the top & bottom rows of the frame
In : df.head()
Out:
A B C D
2013-01-010.469112 -0.282863 -1.509059 -1.135632
2013-01-021.212112 -0.1732150.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.4949291.071804
2013-01-040.721555 -0.706771 -1.0395750.271860
2013-01-05 -0.4249720.5670200.276232 -1.087401
In : df.tail(3)
Out:
A B C D
2013-01-040.721555 -0.706771 -1.0395750.271860
2013-01-05 -0.4249720.5670200.276232 -1.087401
2013-01-06 -0.6736900.113648 -1.4784270.524988
Display the index,columns, and the underlying numpy data
In : df.index
Out:
<class 'pandas.tseries.index.DatetimeIndex'>
Length: 6, Freq: D, Timezone: None
In : df.columns
Out: Index(, dtype=object)
In : df.values
Out:
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 : df.describe()
Out:
A B C D
count6.0000006.0000006.0000006.000000
mean 0.073711 -0.431125 -0.687758 -0.233103
std 0.8431570.9228180.7798870.973118
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% 0.6584440.041933 -0.0343260.461706
max 1.2121120.5670200.2762321.071804
Transposing your data
In : df.T
Out:
2013-01-012013-01-022013-01-032013-01-042013-01-052013-01-06
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 : df.sort_index(axis=1, ascending=False)
Out:
D C B A
2013-01-01 -1.135632 -1.509059 -0.2828630.469112
2013-01-02 -1.0442360.119209 -0.1732151.212112
2013-01-031.071804 -0.494929 -2.104569 -0.861849
2013-01-040.271860 -1.039575 -0.7067710.721555
2013-01-05 -1.0874010.2762320.567020 -0.424972
2013-01-060.524988 -1.4784270.113648 -0.673690
Sorting by values
In : df.sort(columns='B')
Out:
A B C D
2013-01-03 -0.861849 -2.104569 -0.4949291.071804
2013-01-040.721555 -0.706771 -1.0395750.271860
2013-01-010.469112 -0.282863 -1.509059 -1.135632
2013-01-021.212112 -0.1732150.119209 -1.044236
2013-01-06 -0.6736900.113648 -1.4784270.524988
2013-01-05 -0.4249720.5670200.276232 -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 section and below.
Getting
Selecting a single column, which yields a Series, equivalent to df.A
In : df['A']
Out:
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 : df[0:3]
Out:
A B C D
2013-01-010.469112 -0.282863 -1.509059 -1.135632
2013-01-021.212112 -0.1732150.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.4949291.071804
In : df['20130102':'20130104']
Out:
A B C D
2013-01-021.212112 -0.1732150.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.4949291.071804
2013-01-040.721555 -0.706771 -1.0395750.271860
Selection by Label
See more in Selection by Label
For getting a cross section using a label
In : df.loc0]]
Out:
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 : df.loc[:,['A','B']]
Out:
A B
2013-01-010.469112 -0.282863
2013-01-021.212112 -0.173215
2013-01-03 -0.861849 -2.104569
2013-01-040.721555 -0.706771
2013-01-05 -0.4249720.567020
2013-01-06 -0.6736900.113648
Showing label slicing, both endpoints are included
In : df.loc['20130102':'20130104',['A','B']]
Out:
A B
2013-01-021.212112 -0.173215
2013-01-03 -0.861849 -2.104569
2013-01-040.721555 -0.706771
Reduction in the dimensions of the returned object
In : df.loc['20130102',['A','B']]
Out:
A 1.212112
B -0.173215
Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value
In : df.loc0],'A']
Out: 0.46911229990718628
For getting fast access to a scalar (equiv to the prior method)
In : df.at0],'A']
Out: 0.46911229990718628
Selection by Position
See more in Selection by Position
Select via the position of the passed integers
In : df.iloc[3]
Out:
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 : df.iloc[3:5,0:2]
Out:
A B
2013-01-040.721555 -0.706771
2013-01-05 -0.4249720.567020
By lists of integer position locations, similar to the numpy/python style
In : df.iloc[[1,2,4],[0,2]]
Out:
A C
2013-01-021.2121120.119209
2013-01-03 -0.861849 -0.494929
2013-01-05 -0.4249720.276232
For slicing rows explicitly
In : df.iloc[1:3,:]
Out:
A B C D
2013-01-021.212112 -0.1732150.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.4949291.071804
For slicing columns explicitly
In : df.iloc[:,1:3]
Out:
B C
2013-01-01 -0.282863 -1.509059
2013-01-02 -0.1732150.119209
2013-01-03 -2.104569 -0.494929
2013-01-04 -0.706771 -1.039575
2013-01-050.5670200.276232
2013-01-060.113648 -1.478427
For getting a value explicity
In : df.iloc[1,1]
Out: -0.17321464905330858
For getting fast access to a scalar (equiv to the prior method)
In : df.iat[1,1]
Out: -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 : x = list('abcdef')
In : x[4:10]
Out: ['e', 'f']
In : x[8:10]
Out: []
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 : df.A > 0]
Out:
A B C D
2013-01-010.469112 -0.282863 -1.509059 -1.135632
2013-01-021.212112 -0.1732150.119209 -1.044236
2013-01-040.721555 -0.706771 -1.0395750.271860
A where operation for getting.
In : df> 0]
Out:
A B C D
2013-01-010.469112 NaN NaN NaN
2013-01-021.212112 NaN0.119209 NaN
2013-01-03 NaN NaN NaN1.071804
2013-01-040.721555 NaN NaN0.271860
2013-01-05 NaN0.5670200.276232 NaN
2013-01-06 NaN0.113648 NaN0.524988
Setting
Setting a new column automatically aligns the data by the indexes
In : s1 = pd.Series([1,2,3,4,5,6],index=date_range('20130102',periods=6))
In : s1
Out:
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 : df['F'] = s1
Setting values by label
In : df.at0],'A'] = 0
Setting values by position
In : df.iat[0,1] = 0
Setting by assigning with a numpy array
In : df.loc[:,'D'] = np.array([5] * len(df))
The result of the prior setting operations
In : df
Out:
A B CD F
2013-01-010.0000000.000000 -1.5090595 NaN
2013-01-021.212112 -0.1732150.1192095 1
2013-01-03 -0.861849 -2.104569 -0.4949295 2
2013-01-040.721555 -0.706771 -1.0395755 3
2013-01-05 -0.4249720.5670200.2762325 4
2013-01-06 -0.6736900.113648 -1.4784275 5
A where operation with setting.
In : df2 = df.copy()
In : df2> 0] = -df2
In : df2
Out:
A B CD F
2013-01-010.0000000.000000 -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
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 : df1 = df.reindex(index=dates[0:4],columns=list(df.columns) + ['E'])
In : df1.loc0]:dates[1],'E'] = 1
In : df1
Out:
A B CD F E
2013-01-010.0000000.000000 -1.5090595 NaN 1
2013-01-021.212112 -0.1732150.1192095 1 1
2013-01-03 -0.861849 -2.104569 -0.4949295 2 NaN
2013-01-040.721555 -0.706771 -1.0395755 3 NaN
To drop any rows that have missing data.
In : df1.dropna(how='any')
Out:
A B CDFE
2013-01-021.212112 -0.1732150.119209511
Filling missing data
In : df1.fillna(value=5)
Out:
A B CDFE
2013-01-010.0000000.000000 -1.509059551
2013-01-021.212112 -0.1732150.119209511
2013-01-03 -0.861849 -2.104569 -0.494929525
2013-01-040.721555 -0.706771 -1.039575535
To get the boolean mask where values are nan
In : pd.isnull(df1)
Out:
A B C D F E
2013-01-01FalseFalseFalseFalse TrueFalse
2013-01-02FalseFalseFalseFalseFalseFalse
2013-01-03FalseFalseFalseFalseFalse True
2013-01-04FalseFalseFalseFalseFalse True
Operations
See the Basic section on Binary Ops
Stats
Operations in general exclude missing data.
Performing a descriptive statistic
In : df.mean()
Out:
A -0.004474
B -0.383981
C -0.687758
D 5.000000
F 3.000000
dtype: float64
Same operation on the other axis
In : df.mean(1)
Out:
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 : s = pd.Series([1,3,5,np.nan,6,8],index=dates).shift(2)
In : s
Out:
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 : df.sub(s,axis='index')
Out:
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
Apply
Applying functions to the data
In : df.apply(np.cumsum)
Out:
A B C D F
2013-01-010.0000000.000000 -1.509059 5 NaN
2013-01-021.212112 -0.173215 -1.38985010 1
2013-01-030.350263 -2.277784 -1.88477915 3
2013-01-041.071818 -2.984555 -2.92435420 6
2013-01-050.646846 -2.417535 -2.6481222510
2013-01-06 -0.026844 -2.303886 -4.1265493015
In : df.apply(lambda x: x.max() - x.min())
Out:
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 : s = Series(np.random.randint(0,7,size=10))
In : s
Out:
0 4
1 2
2 1
3 2
4 6
5 4
6 4
7 6
8 4
9 4
dtype: int64
In : s.value_counts()
Out:
4 5
6 2
2 2
1 1
dtype: int64
String Methods
See more at Vectorized String Methods
In : s = Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
In : s.str.lower()
Out:
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 : df = pd.DataFrame(np.random.randn(10, 4))
In : df
Out:
0 1 2 3
0 -0.5487021.467327 -1.015962 -0.483075
11.637550 -1.217659 -0.291519 -1.745505
2 -0.2639520.991460 -0.9190690.266046
3 -0.7096611.6690521.037882 -1.705775
4 -0.919854 -0.0423791.247642 -0.009920
50.2902130.4957670.3629491.548106
6 -1.131345 -0.0893290.337863 -0.945867
7 -0.9321321.9560300.017587 -0.016692
8 -0.5752470.254161 -1.1437040.215897
91.193555 -0.077118 -0.408530 -0.862495
# break it into pieces
In : pieces = 3], df[3:7], df[7:]]
In : concat(pieces)
Out:
0 1 2 3
0 -0.5487021.467327 -1.015962 -0.483075
11.637550 -1.217659 -0.291519 -1.745505
2 -0.2639520.991460 -0.9190690.266046
3 -0.7096611.6690521.037882 -1.705775
4 -0.919854 -0.0423791.247642 -0.009920
50.2902130.4957670.3629491.548106
6 -1.131345 -0.0893290.337863 -0.945867
7 -0.9321321.9560300.017587 -0.016692
8 -0.5752470.254161 -1.1437040.215897
91.193555 -0.077118 -0.408530 -0.862495
Join
SQL style merges. See the Database style joining
In : left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
In : right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
In : left
Out:
keylval
0foo 1
1foo 2
In : right
Out:
keyrval
0foo 4
1foo 5
In : merge(left, right, on='key')
Out:
keylvalrval
0foo 1 4
1foo 1 5
2foo 2 4
3foo 2 5
Append
Append rows to a dataframe. See the Appending
In : df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])
In : df
Out:
A B C D
01.3460611.5117631.627081 -0.990582
1 -0.4416521.2115260.2685200.024580
2 -1.5775850.396823 -0.105381 -0.532532
31.4537491.208843 -0.080952 -0.264610
4 -0.727965 -0.5893460.339969 -0.693205
5 -0.3393550.5936160.8843451.591431
60.1418090.2203900.4355890.192451
7 -0.0967010.8033511.715071 -0.708758
In : s = df.iloc[3]
In : df.append(s, ignore_index=True)
Out:
A B C D
01.3460611.5117631.627081 -0.990582
1 -0.4416521.2115260.2685200.024580
2 -1.5775850.396823 -0.105381 -0.532532
31.4537491.208843 -0.080952 -0.264610
4 -0.727965 -0.5893460.339969 -0.693205
5 -0.3393550.5936160.8843451.591431
60.1418090.2203900.4355890.192451
7 -0.0967010.8033511.715071 -0.708758
81.4537491.208843 -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 : df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
....: 'foo', 'bar', 'foo', 'foo'],
....: 'B' : ['one', 'one', 'two', 'three',
....: 'two', 'two', 'one', 'three'],
....: 'C' : randn(8), 'D' : randn(8)})
....:
In : df
Out:
A B C D
0foo one -1.202872 -0.055224
1bar one -1.8144702.395985
2foo two1.0186011.552825
3barthree -0.5954470.166599
4foo two1.3954330.047609
5bar two -0.392670 -0.136473
6foo one0.007207 -0.561757
7foothree1.928123 -1.623033
Grouping and then applying a function sum to the resulting groups.
In : df.groupby('A').sum()
Out:
C D
A
bar -2.8025882.42611
foo3.146492 -0.63958
Grouping by multiple columns forms a hierarchical index, which we then apply the function.
In : df.groupby(['A','B']).sum()
Out:
C D
A B
bar one -1.8144702.395985
three -0.5954470.166599
two -0.392670 -0.136473
foo one -1.195665 -0.616981
three1.928123 -1.623033
two 2.4140341.600434
Reshaping
See the section on Hierarchical Indexing and see
the section on Reshaping).
Stack
In : tuples = zip(*[['bar', 'bar', 'baz', 'baz',
....: 'foo', 'foo', 'qux', 'qux'],
....: ['one', 'two', 'one', 'two',
....: 'one', 'two', 'one', 'two']])
....:
In : index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
In : df = pd.DataFrame(randn(8, 2), index=index, columns=['A', 'B'])
In : df2 = df[:4]
In : df2
Out:
A B
first second
bar one 0.029399 -0.542108
two 0.282696 -0.087302
baz one -1.5751701.771208
two 0.8164821.100230
The stack function “compresses” a level in the DataFrame’s columns.
In : stacked = df2.stack()
In : stacked
Out:
firstsecond
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 : stacked.unstack()
Out:
A B
first second
bar one 0.029399 -0.542108
two 0.282696 -0.087302
baz one -1.5751701.771208
two 0.8164821.100230
In : stacked.unstack(1)
Out:
second one two
first
bar A0.0293990.282696
B -0.542108 -0.087302
baz A -1.5751700.816482
B1.7712081.100230
In : stacked.unstack(0)
Out:
first bar baz
second
one A0.029399 -1.575170
B -0.5421081.771208
two A0.2826960.816482
B -0.0873021.100230
Pivot Tables
See the section on Pivot Tables.
In : df = 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 : df
Out:
AB C D E
0 oneAfoo1.418757 -0.179666
1 oneBfoo -1.8790241.291836
2 twoCfoo0.536826 -0.009614
3 threeAbar1.0061600.392149
4 oneBbar -0.0297160.264599
5 oneCbar -1.146178 -0.057409
6 twoAfoo0.100900 -1.425638
7 threeBfoo -1.0350181.024098
8 oneCfoo0.314665 -0.106062
9 oneAbar -0.7737231.824375
10 twoBbar -1.1706530.595974
11threeCbar0.6487401.167115
We can produce pivot tables from this data very easily:
In : pivot_table(df, values='D', rows=['A', 'B'], cols=['C'])
Out:
C bar foo
A B
one A -0.7737231.418757
B -0.029716 -1.879024
C -1.1461780.314665
three A1.006160 NaN
B NaN -1.035018
C0.648740 NaN
two A NaN0.100900
B -1.170653 NaN
C NaN0.536826
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 : rng = pd.date_range('1/1/2012', periods=100, freq='S')
In : ts = pd.Series(randint(0, 500, len(rng)), index=rng)
In : ts.resample('5Min', how='sum')
Out:
2012-01-01 25083
Freq: 5T, dtype: int64
Time zone representation
In : rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
In : ts = pd.Series(randn(len(rng)), rng)
In : ts_utc = ts.tz_localize('UTC')
In : ts_utc
Out:
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 : ts_utc.tz_convert('US/Eastern')
Out:
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 : rng = pd.date_range('1/1/2012', periods=5, freq='M')
In : ts = pd.Series(randn(len(rng)), index=rng)
In : ts
Out:
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 : ps = ts.to_period()
In : ps
Out:
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 : ps.to_timestamp()
Out:
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 : prng = period_range('1990Q1', '2000Q4', freq='Q-NOV')
In : ts = Series(randn(len(prng)), prng)
In : ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
In : ts.head()
Out:
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 : ts = pd.Series(randn(1000), index=pd.date_range('1/1/2000', periods=1000))
In : ts = ts.cumsum()
In : ts.plot()
Out: <matplotlib.axes.AxesSubplot at 0x40d5110>
On DataFrame, plot is a convenience to plot all of the columns with labels:
In : df = pd.DataFrame(randn(1000, 4), index=ts.index,
.....: columns=['A', 'B', 'C', 'D'])
.....:
In : df = df.cumsum()
In : plt.figure(); df.plot(); plt.legend(loc='best')
Out: <matplotlib.legend.Legend at 0x4321d90>
Getting Data In/Out
CSV
Writing to a csv file
In : df.to_csv('foo.csv')
Reading from a csv file
In : pd.read_csv('foo.csv')
Out:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1000 entries, 0 to 999
Data columns (total 5 columns):
Unnamed: 0 1000non-null values
A 1000non-null values
B 1000non-null values
C 1000non-null values
D 1000non-null values
dtypes: float64(4), object(1)
HDF5
Reading and writing to HDFStores
Writing to a HDF5 Store
In : df.to_hdf('foo.h5','df')
Reading from a HDF5 Store
In : read_hdf('foo.h5','df')
Out:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1000 entries, 2000-01-01 00:00:00 to 2002-09-26 00:00:00
Freq: D
Data columns (total 4 columns):
A 1000non-null values
B 1000non-null values
C 1000non-null values
D 1000non-null values
dtypes: float64(4)
Excel
Reading and writing to MS Excel
Writing to an excel file
In : df.to_excel('foo.xlsx', sheet_name='sheet1')
Reading from an excel file
In : xls = ExcelFile('foo.xlsx')
In : xls.parse('sheet1', index_col=None, na_values=['NA'])
Out:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1000 entries, 2000-01-01 00:00:00 to 2002-09-26 00:00:00
Data columns (total 4 columns):
A 1000non-null values
B 1000non-null values
C 1000non-null values
D 1000non-null values
dtypes: float64(4)
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