Pandas 入门

整理了Pandas的一些基础用法,以前学习的时候写在Jupyter上。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
s=pd.Series([1,2,3,np.nan,5,6])
s
0    1.0
1    2.0
2    3.0
3    NaN
4    5.0
5    6.0
dtype: float64
dates=pd.date_range("20170101",periods=20)
dates
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
               '2017-01-05', '2017-01-06', '2017-01-07', '2017-01-08',
               '2017-01-09', '2017-01-10', '2017-01-11', '2017-01-12',
               '2017-01-13', '2017-01-14', '2017-01-15', '2017-01-16',
               '2017-01-17', '2017-01-18', '2017-01-19', '2017-01-20'],
              dtype='datetime64[ns]', freq='D')
np.random.randint(10,size=(2,5))
array([[6, 4, 1, 8, 0],
       [0, 4, 3, 0, 1]])
df1=pd.DataFrame(np.random.randn(20,4),index=dates,columns=["a","b","c","d"])
df1
a b c d
2017-01-01 -0.731296 0.308452 1.550586 0.022510
2017-01-02 0.011909 -2.604560 -0.328210 -0.831059
2017-01-03 1.016461 -0.340761 1.399342 1.435456
2017-01-04 -0.610496 -0.962359 -0.397980 0.833718
2017-01-05 1.298400 -0.148515 -1.366670 0.718973
2017-01-06 0.265090 0.490953 1.048929 -0.611945
2017-01-07 -0.718811 1.640064 -1.063297 -1.092510
2017-01-08 -0.686471 0.541123 0.415082 0.368303
2017-01-09 0.352406 -0.061781 1.385387 0.240791
2017-01-10 -0.750252 -0.353765 0.163297 -0.706397
2017-01-11 1.707390 1.000258 0.717216 -0.566941
2017-01-12 -0.341289 0.742661 -1.820184 -0.182327
2017-01-13 -0.583300 -0.490837 -0.611798 -1.238514
2017-01-14 0.285966 1.219942 1.679262 0.170911
2017-01-15 -0.100615 -0.111391 1.827916 0.359999
2017-01-16 0.834599 0.214739 -0.868497 0.637817
2017-01-17 1.233904 -0.296525 -0.218316 0.651542
2017-01-18 2.211547 1.652226 1.415402 -1.023644
2017-01-19 0.176992 0.228890 0.844449 1.267496
2017-01-20 -0.242708 -0.792746 0.377210 0.521628
df2=pd.DataFrame({"A":range(11,20),"B":pd.Timestamp("20170101"),"C":pd.Series(np.random.random(9),index=range(11,20)),"D":np.array([i for i in range(1,10)]),
                 "E":pd.Categorical(["one","two","three","four","five","six","seven","eight","nine"])})
df2
A B C D E
11 11 2017-01-01 0.115527 1 one
12 12 2017-01-01 0.966706 2 two
13 13 2017-01-01 0.627952 3 three
14 14 2017-01-01 0.858634 4 four
15 15 2017-01-01 0.512081 5 five
16 16 2017-01-01 0.986166 6 six
17 17 2017-01-01 0.963712 7 seven
18 18 2017-01-01 0.754798 8 eight
19 19 2017-01-01 0.184086 9 nine

基础操作

df2.head()
A B C D E
11 11 2017-01-01 0.115527 1 one
12 12 2017-01-01 0.966706 2 two
13 13 2017-01-01 0.627952 3 three
14 14 2017-01-01 0.858634 4 four
15 15 2017-01-01 0.512081 5 five
df2.tail(2)
A B C D E
18 18 2017-01-01 0.754798 8 eight
19 19 2017-01-01 0.184086 9 nine
df2.index
Int64Index([11, 12, 13, 14, 15, 16, 17, 18, 19], dtype='int64')
df2.columns
Index([u'A', u'B', u'C', u'D', u'E'], dtype='object')
df2.values
array([[11L, Timestamp('2017-01-01 00:00:00'), 0.11552714551146004, 1,
        'one'],
       [12L, Timestamp('2017-01-01 00:00:00'), 0.9667064772135637, 2, 'two'],
       [13L, Timestamp('2017-01-01 00:00:00'), 0.6279515096467739, 3,
        'three'],
       [14L, Timestamp('2017-01-01 00:00:00'), 0.8586339869062394, 4,
        'four'],
       [15L, Timestamp('2017-01-01 00:00:00'), 0.5120808880213029, 5,
        'five'],
       [16L, Timestamp('2017-01-01 00:00:00'), 0.9861662616379155, 6, 'six'],
       [17L, Timestamp('2017-01-01 00:00:00'), 0.9637117377995325, 7,
        'seven'],
       [18L, Timestamp('2017-01-01 00:00:00'), 0.7547980447750812, 8,
        'eight'],
       [19L, Timestamp('2017-01-01 00:00:00'), 0.1840863987352963, 9,
        'nine']], dtype=object)
df2.describe()
A C D
count 9.000000 9.000000 9.000000
mean 15.000000 0.663296 5.000000
std 2.738613 0.332958 2.738613
min 11.000000 0.115527 1.000000
25% 13.000000 0.512081 3.000000
50% 15.000000 0.754798 5.000000
75% 17.000000 0.963712 7.000000
max 19.000000 0.986166 9.000000
df2.T
11 12 13 14 15 16 17 18 19
A 11 12 13 14 15 16 17 18 19
B 2017-01-01 00:00:00 2017-01-01 00:00:00 2017-01-01 00:00:00 2017-01-01 00:00:00 2017-01-01 00:00:00 2017-01-01 00:00:00 2017-01-01 00:00:00 2017-01-01 00:00:00 2017-01-01 00:00:00
C 0.115527 0.966706 0.627952 0.858634 0.512081 0.986166 0.963712 0.754798 0.184086
D 1 2 3 4 5 6 7 8 9
E one two three four five six seven eight nine
df2.sort_index(axis=1,ascending=False)
E D C B A
11 one 1 0.115527 2017-01-01 11
12 two 2 0.966706 2017-01-01 12
13 three 3 0.627952 2017-01-01 13
14 four 4 0.858634 2017-01-01 14
15 five 5 0.512081 2017-01-01 15
16 six 6 0.986166 2017-01-01 16
17 seven 7 0.963712 2017-01-01 17
18 eight 8 0.754798 2017-01-01 18
19 nine 9 0.184086 2017-01-01 19
df2.sort_values(by="C")
A B C D E
11 11 2017-01-01 0.115527 1 one
19 19 2017-01-01 0.184086 9 nine
15 15 2017-01-01 0.512081 5 five
13 13 2017-01-01 0.627952 3 three
18 18 2017-01-01 0.754798 8 eight
14 14 2017-01-01 0.858634 4 four
17 17 2017-01-01 0.963712 7 seven
12 12 2017-01-01 0.966706 2 two
16 16 2017-01-01 0.986166 6 six

通过标签选择

loc与at相似,但是at只能选取一个值,而loc可以选取行或列

df2.loc[11]
A                     11
B    2017-01-01 00:00:00
C               0.115527
D                      1
E                    one
Name: 11, dtype: object
df2.loc[:,["A","B"]]
A B
11 11 2017-01-01
12 12 2017-01-01
13 13 2017-01-01
14 14 2017-01-01
15 15 2017-01-01
16 16 2017-01-01
17 17 2017-01-01
18 18 2017-01-01
19 19 2017-01-01
df2.loc[13,"C"]
0.62795150964677393

通过位置选择

df2.iloc[0]
A                     11
B    2017-01-01 00:00:00
C               0.115527
D                      1
E                    one
Name: 11, dtype: object
df2.iloc[:3,0:3]
A B C
11 11 2017-01-01 0.115527
12 12 2017-01-01 0.966706
13 13 2017-01-01 0.627952
df2.iloc[[1,3,5],[1,3]]
B D
12 2017-01-01 2
14 2017-01-01 4
16 2017-01-01 6
df2.iloc[1,2]
0.96670647721356373

布尔索引

df2[df2.C>0.5]
A B C D E
12 12 2017-01-01 0.966706 2 two
13 13 2017-01-01 0.627952 3 three
14 14 2017-01-01 0.858634 4 four
15 15 2017-01-01 0.512081 5 five
16 16 2017-01-01 0.986166 6 six
17 17 2017-01-01 0.963712 7 seven
18 18 2017-01-01 0.754798 8 eight
df1[ df1 > 0]
a b c d
2017-01-01 NaN 0.308452 1.550586 0.022510
2017-01-02 0.011909 NaN NaN NaN
2017-01-03 1.016461 NaN 1.399342 1.435456
2017-01-04 NaN NaN NaN 0.833718
2017-01-05 1.298400 NaN NaN 0.718973
2017-01-06 0.265090 0.490953 1.048929 NaN
2017-01-07 NaN 1.640064 NaN NaN
2017-01-08 NaN 0.541123 0.415082 0.368303
2017-01-09 0.352406 NaN 1.385387 0.240791
2017-01-10 NaN NaN 0.163297 NaN
2017-01-11 1.707390 1.000258 0.717216 NaN
2017-01-12 NaN 0.742661 NaN NaN
2017-01-13 NaN NaN NaN NaN
2017-01-14 0.285966 1.219942 1.679262 0.170911
2017-01-15 NaN NaN 1.827916 0.359999
2017-01-16 0.834599 0.214739 NaN 0.637817
2017-01-17 1.233904 NaN NaN 0.651542
2017-01-18 2.211547 1.652226 1.415402 NaN
2017-01-19 0.176992 0.228890 0.844449 1.267496
2017-01-20 NaN NaN 0.377210 0.521628
df2[df2['E'].isin(["one","six"])]
A B C D E
11 11 2017-01-01 0.115527 1 one
16 16 2017-01-01 0.986166 6 six
df2[df2.E.isin(['one',"six"])]
A B C D E
11 11 2017-01-01 0.115527 1 one
16 16 2017-01-01 0.986166 6 six
s1=pd.Series([111,22,33,44],index=range(12,16))
df2["F"]=s1
df2
A B C D E F
11 11 2017-01-01 0.115527 1 one NaN
12 12 2017-01-01 0.966706 2 two 111.0
13 13 2017-01-01 0.627952 3 three 22.0
14 14 2017-01-01 0.858634 4 four 33.0
15 15 2017-01-01 0.512081 5 five 44.0
16 16 2017-01-01 0.986166 6 six NaN
17 17 2017-01-01 0.963712 7 seven NaN
18 18 2017-01-01 0.754798 8 eight NaN
19 19 2017-01-01 0.184086 9 nine NaN
df2.at[17,"D"]=3
print df2
df2.iat[1,0]=1
print df2
print len(df2)
df2['F']=np.array([5]*len(df2))
print df2
     A          B         C  D      E      F
11  11 2017-01-01  0.115527  1    one    NaN
12  12 2017-01-01  0.966706  2    two  111.0
13  13 2017-01-01  0.627952  3  three   22.0
14  14 2017-01-01  0.858634  4   four   33.0
15  15 2017-01-01  0.512081  5   five   44.0
16  16 2017-01-01  0.986166  6    six    NaN
17  17 2017-01-01  0.963712  3  seven    NaN
18  18 2017-01-01  0.754798  8  eight    NaN
19  19 2017-01-01  0.184086  9   nine    NaN
     A          B         C  D      E      F
11  11 2017-01-01  0.115527  1    one    NaN
12   1 2017-01-01  0.966706  2    two  111.0
13  13 2017-01-01  0.627952  3  three   22.0
14  14 2017-01-01  0.858634  4   four   33.0
15  15 2017-01-01  0.512081  5   five   44.0
16  16 2017-01-01  0.986166  6    six    NaN
17  17 2017-01-01  0.963712  3  seven    NaN
18  18 2017-01-01  0.754798  8  eight    NaN
19  19 2017-01-01  0.184086  9   nine    NaN
9
     A          B         C  D      E  F
11  11 2017-01-01  0.115527  1    one  5
12   1 2017-01-01  0.966706  2    two  5
13  13 2017-01-01  0.627952  3  three  5
14  14 2017-01-01  0.858634  4   four  5
15  15 2017-01-01  0.512081  5   five  5
16  16 2017-01-01  0.986166  6    six  5
17  17 2017-01-01  0.963712  3  seven  5
18  18 2017-01-01  0.754798  8  eight  5
19  19 2017-01-01  0.184086  9   nine  5
df3=df2.copy()
df3["B"]=df3["E"]=6
df3[df3>0]=-df3
print df3
df3.iloc[1:3,1:3]=np.nan
print df3.isnull()
print np.isnan(df3.iloc[1,1])
     A  B         C  D  E  F
11 -11 -6 -0.115527 -1 -6 -5
12  -1 -6 -0.966706 -2 -6 -5
13 -13 -6 -0.627952 -3 -6 -5
14 -14 -6 -0.858634 -4 -6 -5
15 -15 -6 -0.512081 -5 -6 -5
16 -16 -6 -0.986166 -6 -6 -5
17 -17 -6 -0.963712 -3 -6 -5
18 -18 -6 -0.754798 -8 -6 -5
19 -19 -6 -0.184086 -9 -6 -5
        A      B      C      D      E      F
11  False  False  False  False  False  False
12  False   True   True  False  False  False
13  False   True   True  False  False  False
14  False  False  False  False  False  False
15  False  False  False  False  False  False
16  False  False  False  False  False  False
17  False  False  False  False  False  False
18  False  False  False  False  False  False
19  False  False  False  False  False  False
True

reindex() 方法可以对指定轴上的索引进行改变/增加/删除操作,这将返回原始数据的一个拷贝

dates=pd.date_range("20170101",periods=len(df2))
print df2
df4=df2.reindex(index=dates,columns=list(df2.columns))
print df4
     A          B         C  D      E  F
11  11 2017-01-01  0.115527  1    one  5
12   1 2017-01-01  0.966706  2    two  5
13  13 2017-01-01  0.627952  3  three  5
14  14 2017-01-01  0.858634  4   four  5
15  15 2017-01-01  0.512081  5   five  5
16  16 2017-01-01  0.986166  6    six  5
17  17 2017-01-01  0.963712  3  seven  5
18  18 2017-01-01  0.754798  8  eight  5
19  19 2017-01-01  0.184086  9   nine  5
             A   B   C   D    E   F
2017-01-01 NaN NaT NaN NaN  NaN NaN
2017-01-02 NaN NaT NaN NaN  NaN NaN
2017-01-03 NaN NaT NaN NaN  NaN NaN
2017-01-04 NaN NaT NaN NaN  NaN NaN
2017-01-05 NaN NaT NaN NaN  NaN NaN
2017-01-06 NaN NaT NaN NaN  NaN NaN
2017-01-07 NaN NaT NaN NaN  NaN NaN
2017-01-08 NaN NaT NaN NaN  NaN NaN
2017-01-09 NaN NaT NaN NaN  NaN NaN
df5=df2.reindex(index=range(14,18),columns=list(df2.columns)+["G"])
print df5
     A          B         C  D      E  F   G
14  14 2017-01-01  0.858634  4   four  5 NaN
15  15 2017-01-01  0.512081  5   five  5 NaN
16  16 2017-01-01  0.986166  6    six  5 NaN
17  17 2017-01-01  0.963712  3  seven  5 NaN
df5.loc[14,"C"]=np.nan
print df5
df5=df5.reindex(columns=list(np.array(["A","B","C","D"])))
print df5
df6=df5.dropna(how="any")
print df5
print df6
df7=df5.fillna(value=1111)
print df7
     A          B         C  D      E  F   G
14  14 2017-01-01       NaN  4   four  5 NaN
15  15 2017-01-01  0.512081  5   five  5 NaN
16  16 2017-01-01  0.986166  6    six  5 NaN
17  17 2017-01-01  0.963712  3  seven  5 NaN
     A          B         C  D
14  14 2017-01-01       NaN  4
15  15 2017-01-01  0.512081  5
16  16 2017-01-01  0.986166  6
17  17 2017-01-01  0.963712  3
     A          B         C  D
14  14 2017-01-01       NaN  4
15  15 2017-01-01  0.512081  5
16  16 2017-01-01  0.986166  6
17  17 2017-01-01  0.963712  3
     A          B         C  D
15  15 2017-01-01  0.512081  5
16  16 2017-01-01  0.986166  6
17  17 2017-01-01  0.963712  3
     A          B            C  D
14  14 2017-01-01  1111.000000  4
15  15 2017-01-01     0.512081  5
16  16 2017-01-01     0.986166  6
17  17 2017-01-01     0.963712  3

相关操作

print df7.mean()
A     15.50000
C    278.36549
D      4.50000
dtype: float64
print df7.mean(1)
14    376.333333
15      6.837360
16      7.662055
17      6.987904
dtype: float64
dates=pd.date_range("2017-01-01",periods=4)
print dates
s2=pd.Series(range(0,len(dates)),index=dates).shift(2)
print s2
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D')
2017-01-01    NaN
2017-01-02    NaN
2017-01-03    0.0
2017-01-04    1.0
Freq: D, dtype: float64
#sub Equivalent to dataframe - other, but with support to substitute a fill_value for missing data 
#in one of the inputs.
dates=pd.date_range("2017-01-01",periods=4)
df7=df7.set_index(dates)
df7.iloc[3,0]=np.nan
df7.iloc[2,2]=np.nan
df7["F"]=np.nan
df7["B"]=13
print df7
df7=df7.sub(s2,axis="index")
print df7
               A   B            C  D   F
2017-01-01  14.0  13  1111.000000  4 NaN
2017-01-02  15.0  13     0.512081  5 NaN
2017-01-03  16.0  13          NaN  6 NaN
2017-01-04   NaN  13     0.963712  3 NaN
               A     B         C    D   F
2017-01-01   NaN   NaN       NaN  NaN NaN
2017-01-02   NaN   NaN       NaN  NaN NaN
2017-01-03  16.0  13.0       NaN  6.0 NaN
2017-01-04   NaN  12.0 -0.036288  2.0 NaN
a = np.array([[11,24,37], [41,51,61]])
print a
print np.cumsum(a)
print np.cumsum(a,axis=0)
print np.cumsum(a,axis=1)
[[11 24 37]
 [41 51 61]]
[ 11  35  72 113 164 225]
[[11 24 37]
 [52 75 98]]
[[ 11  35  72]
 [ 41  92 153]]
print df1
df1.apply(np.cumsum)
                   a         b         c         d
2017-01-01 -0.731296  0.308452  1.550586  0.022510
2017-01-02  0.011909 -2.604560 -0.328210 -0.831059
2017-01-03  1.016461 -0.340761  1.399342  1.435456
2017-01-04 -0.610496 -0.962359 -0.397980  0.833718
2017-01-05  1.298400 -0.148515 -1.366670  0.718973
2017-01-06  0.265090  0.490953  1.048929 -0.611945
2017-01-07 -0.718811  1.640064 -1.063297 -1.092510
2017-01-08 -0.686471  0.541123  0.415082  0.368303
2017-01-09  0.352406 -0.061781  1.385387  0.240791
2017-01-10 -0.750252 -0.353765  0.163297 -0.706397
2017-01-11  1.707390  1.000258  0.717216 -0.566941
2017-01-12 -0.341289  0.742661 -1.820184 -0.182327
2017-01-13 -0.583300 -0.490837 -0.611798 -1.238514
2017-01-14  0.285966  1.219942  1.679262  0.170911
2017-01-15 -0.100615 -0.111391  1.827916  0.359999
2017-01-16  0.834599  0.214739 -0.868497  0.637817
2017-01-17  1.233904 -0.296525 -0.218316  0.651542
2017-01-18  2.211547  1.652226  1.415402 -1.023644
2017-01-19  0.176992  0.228890  0.844449  1.267496
2017-01-20 -0.242708 -0.792746  0.377210  0.521628
a b c d
2017-01-01 -0.731296 0.308452 1.550586 0.022510
2017-01-02 -0.719387 -2.296108 1.222376 -0.808550
2017-01-03 0.297075 -2.636869 2.621718 0.626906
2017-01-04 -0.313422 -3.599228 2.223738 1.460624
2017-01-05 0.984978 -3.747743 0.857069 2.179597
2017-01-06 1.250069 -3.256790 1.905998 1.567652
2017-01-07 0.531258 -1.616726 0.842700 0.475142
2017-01-08 -0.155213 -1.075604 1.257782 0.843444
2017-01-09 0.197193 -1.137385 2.643169 1.084235
2017-01-10 -0.553059 -1.491150 2.806466 0.377838
2017-01-11 1.154331 -0.490892 3.523682 -0.189103
2017-01-12 0.813042 0.251769 1.703498 -0.371430
2017-01-13 0.229742 -0.239067 1.091700 -1.609944
2017-01-14 0.515708 0.980875 2.770962 -1.439033
2017-01-15 0.415093 0.869484 4.598878 -1.079034
2017-01-16 1.249693 1.084223 3.730380 -0.441217
2017-01-17 2.483597 0.787698 3.512064 0.210325
2017-01-18 4.695144 2.439923 4.927466 -0.813319
2017-01-19 4.872136 2.668814 5.771915 0.454177
2017-01-20 4.629428 1.876068 6.149125 0.975805
df1.apply(lambda x:x.max()-x.min())
a    2.961799
b    4.256786
c    3.648100
d    2.673969
dtype: float64
s3=pd.Series(np.random.randint(0,7,size=10))
print s3
#Returns object containing counts of unique values.
s3.value_counts()
0    1
1    3
2    3
3    3
4    4
5    4
6    5
7    0
8    6
9    1
dtype: int32





3    3
4    2
1    2
6    1
5    1
0    1
dtype: int64
s=pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
s
0       A
1       B
2       C
3    Aaba
4    Baca
5     NaN
6    CABA
7     dog
8     cat
dtype: object
s.str.upper()
0       A
1       B
2       C
3    AABA
4    BACA
5     NaN
6    CABA
7     DOG
8     CAT
dtype: object
df1=pd.DataFrame(np.random.randn(10,4))
df1
0 1 2 3
0 -0.932914 -1.888419 0.382424 -0.818447
1 0.610921 1.115466 -0.198243 0.729085
2 0.000842 -1.159236 0.979033 0.375346
3 -0.634415 -0.204233 2.395083 -0.647589
4 -1.354108 -0.278728 1.042867 1.286785
5 0.914505 -0.664796 1.112920 -0.094563
6 -0.167151 -1.519254 0.015029 -0.567899
7 -0.225384 -0.293270 0.209918 -0.205145
8 0.562184 -0.706002 -0.786689 0.780558
9 -0.075450 -0.983625 -0.053178 -1.989312
#concat 合并
pieces=[df1[:3],df1[:3]]
pd.concat(pieces)
0 1 2 3
0 -0.932914 -1.888419 0.382424 -0.818447
1 0.610921 1.115466 -0.198243 0.729085
2 0.000842 -1.159236 0.979033 0.375346
0 -0.932914 -1.888419 0.382424 -0.818447
1 0.610921 1.115466 -0.198243 0.729085
2 0.000842 -1.159236 0.979033 0.375346
left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1,2]})
right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4,5]})
left
key lval
0 foo 1
1 bar 2
right
key rval
0 foo 4
1 bar 5
pd.merge(left,right,on="key")
key lval rval
0 foo 1 4
1 bar 2 5
df1
0 1 2 3
0 -0.932914 -1.888419 0.382424 -0.818447
1 0.610921 1.115466 -0.198243 0.729085
2 0.000842 -1.159236 0.979033 0.375346
3 -0.634415 -0.204233 2.395083 -0.647589
4 -1.354108 -0.278728 1.042867 1.286785
5 0.914505 -0.664796 1.112920 -0.094563
6 -0.167151 -1.519254 0.015029 -0.567899
7 -0.225384 -0.293270 0.209918 -0.205145
8 0.562184 -0.706002 -0.786689 0.780558
9 -0.075450 -0.983625 -0.053178 -1.989312
left.append(right,ignore_index=True)
key lval rval
0 foo 1.0 NaN
1 bar 2.0 NaN
2 foo NaN 4.0
3 bar NaN 5.0
df1=pd.DataFrame({"A":["one","two","one","two","one","two","one","two"],"B":np.random.randn(8),"C":[3,4,5,2,1,2,1,2]})
df1.groupby("A").sum()
B C
A
one 1.358529 10
two 0.522721 10
df1.groupby(["A","C"]).sum()
B
A C
one 1 0.249465
3 -0.328484
5 1.437548
two 2 -0.171024
4 0.693745
df1=pd.DataFrame(np.random.randn(1000,4),index=pd.date_range("20170101",periods=1000),columns=["A","B","C","D"])
df1=df1.cumsum()
df1.plot()

df1=df1.cumsum()
df1.plot()

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