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10分钟 to pandas

分类: 文章 • 2024-06-29 19:17:22 
import pandas as pd
import numpy as np
import matplotlib.pyplot as pltx

In [3]:

dates = pd.date_range('20130101', periods=6)
dates

Out[3]:

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 [4]:

df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
df

Out[4]:

  A B C D
2013-01-01 0.043660 0.914219 1.364281 0.960460
2013-01-02 0.245818 0.582317 0.456372 -0.734680
2013-01-03 -0.997398 -0.476202 0.967015 0.089730
2013-01-04 -1.132148 0.867161 0.458086 0.797743
2013-01-05 -1.226727 1.524988 -1.980305 0.694533
2013-01-06 1.695086 0.796078 -0.688947 -0.910752

In [5]:

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' })
df2

Out[5]:

  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

In [6]:

df2.dtypes

Out[6]:

A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object

In [11]:

df.describe()

Out[11]:

  A B C D
count 6.000000 6.000000 6.000000 6.000000
mean -0.228618 0.701427 0.096084 0.149506
std 1.131678 0.657413 1.229257 0.810483
min -1.226727 -0.476202 -1.980305 -0.910752
25% -1.098461 0.635757 -0.402617 -0.528577
50% -0.476869 0.831620 0.457229 0.392131
75% 0.195279 0.902454 0.839783 0.771940
max 1.695086 1.524988 1.364281 0.960460

In [12]:

df.T

Out[12]:

  2013-01-01 00:00:00 2013-01-02 00:00:00 2013-01-03 00:00:00 2013-01-04 00:00:00 2013-01-05 00:00:00 2013-01-06 00:00:00
A 0.043660 0.245818 -0.997398 -1.132148 -1.226727 1.695086
B 0.914219 0.582317 -0.476202 0.867161 1.524988 0.796078
C 1.364281 0.456372 0.967015 0.458086 -1.980305 -0.688947
D 0.960460 -0.734680 0.089730 0.797743 0.694533 -0.910752

In [13]:

df.sort_index(axis=1, ascending=False)

Out[13]:

  D C B A
2013-01-01 0.960460 1.364281 0.914219 0.043660
2013-01-02 -0.734680 0.456372 0.582317 0.245818
2013-01-03 0.089730 0.967015 -0.476202 -0.997398
2013-01-04 0.797743 0.458086 0.867161 -1.132148
2013-01-05 0.694533 -1.980305 1.524988 -1.226727
2013-01-06 -0.910752 -0.688947 0.796078 1.695086

In [14]:

df.sort_values(by='B')

Out[14]:

  A B C D
2013-01-03 -0.997398 -0.476202 0.967015 0.089730
2013-01-02 0.245818 0.582317 0.456372 -0.734680
2013-01-06 1.695086 0.796078 -0.688947 -0.910752
2013-01-04 -1.132148 0.867161 0.458086 0.797743
2013-01-01 0.043660 0.914219 1.364281 0.960460
2013-01-05 -1.226727 1.524988 -1.980305 0.694533

In [15]:

df.apply(np.cumsum)

Out[15]:

  A B C D
2013-01-01 0.043660 0.914219 1.364281 0.960460
2013-01-02 0.289478 1.496535 1.820653 0.225781
2013-01-03 -0.707920 1.020334 2.787668 0.315511
2013-01-04 -1.840068 1.887495 3.245754 1.113254
2013-01-05 -3.066794 3.412483 1.265449 1.807786
2013-01-06 -1.371708 4.208561 0.576502 0.897035

In [16]:

df.apply(lambda x: x.max() - x.min())

Out[16]:

A    2.921813
B    2.001190
C    3.344586
D    1.871212
dtype: float64

In [18]:

s = pd.Series(np.random.randint(0, 7, size=10))
s

Out[18]:

0    3
1    3
2    3
3    4
4    3
5    1
6    6
7    3
8    1
9    4
dtype: int32

In [19]:

s.value_counts()

Out[19]:

3    5
4    2
1    2
6    1
dtype: int64

In [20]:

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

Out[20]:

0       a
1       b
2       c
3    aaba
4    baca
5     NaN
6    caba
7     dog
8     cat
dtype: object

In [21]:

df = pd.DataFrame(np.random.randn(10, 4))
df

Out[21]:

  0 1 2 3
0 0.625507 -0.587885 0.933430 -1.608638
1 -0.570564 1.653211 -0.169102 0.278487
2 0.587255 0.580736 1.738137 -1.989709
3 1.238611 0.867963 -0.679791 1.784103
4 -0.190212 -1.065888 0.629625 0.968308
5 -1.123788 0.433555 0.613186 -0.885229
6 -0.872940 0.117758 1.085781 -1.739964
7 -1.064637 -1.562930 0.609092 0.591197
8 -0.479885 0.019224 -0.071010 0.203683
9 1.562199 -0.084385 0.169083 0.588641

In [24]:

pieces = [df[:3], df[3:7]]
pieces

Out[24]:

[          0         1         2         3
 0  0.625507 -0.587885  0.933430 -1.608638
 1 -0.570564  1.653211 -0.169102  0.278487
 2  0.587255  0.580736  1.738137 -1.989709,
           0         1         2         3
 3  1.238611  0.867963 -0.679791  1.784103
 4 -0.190212 -1.065888  0.629625  0.968308
 5 -1.123788  0.433555  0.613186 -0.885229
 6 -0.872940  0.117758  1.085781 -1.739964]

In [25]:

pd.concat(pieces)

Out[25]:

  0 1 2 3
0 0.625507 -0.587885 0.933430 -1.608638
1 -0.570564 1.653211 -0.169102 0.278487
2 0.587255 0.580736 1.738137 -1.989709
3 1.238611 0.867963 -0.679791 1.784103
4 -0.190212 -1.065888 0.629625 0.968308
5 -1.123788 0.433555 0.613186 -0.885229
6 -0.872940 0.117758 1.085781 -1.739964

In [26]:

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

Out[26]:

  key lval
0 foo 1
1 foo 2

In [27]:

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

Out[27]:

  key rval
0 foo 4
1 foo 5

In [28]:

pd.merge(left, right, on='key')

Out[28]:

  key lval rval
0 foo 1 4
1 foo 1 5
2 foo 2 4
3 foo 2 5

追加

In [29]:

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

Out[29]:

  A B C D
0 0.567299 -0.835836 1.997388 1.219204
1 0.901536 -0.172666 -0.396798 1.102758
2 0.585077 -0.283219 0.685800 0.284514
3 0.228130 0.916432 0.102306 -1.420889
4 1.196156 -2.166493 1.202548 0.198905
5 0.930729 0.735202 0.780484 1.035269
6 0.335974 -0.364062 1.316474 -0.390350
7 1.611419 -0.277883 0.116712 -0.781101

In [30]:

s = df.iloc[3]
s

Out[30]:

A    0.228130
B    0.916432
C    0.102306
D   -1.420889
Name: 3, dtype: float64

In [31]:

df.append(s, ignore_index=True)

Out[31]:

  A B C D
0 0.567299 -0.835836 1.997388 1.219204
1 0.901536 -0.172666 -0.396798 1.102758
2 0.585077 -0.283219 0.685800 0.284514
3 0.228130 0.916432 0.102306 -1.420889
4 1.196156 -2.166493 1.202548 0.198905
5 0.930729 0.735202 0.780484 1.035269
6 0.335974 -0.364062 1.316474 -0.390350
7 1.611419 -0.277883 0.116712 -0.781101
8 0.228130 0.916432 0.102306 -1.420889

分组

In [32]:

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)})
df

Out[32]:

  A B C D
0 foo one 1.946394 -1.603756
1 bar one 1.128497 0.186203
2 foo two 0.768784 -1.485998
3 bar three 1.530352 1.159454
4 foo two 0.740245 -0.517180
5 bar two -1.475944 2.510523
6 foo one -0.822403 0.036043
7 foo three -0.799283 -2.370425

In [33]:

df.groupby('A').sum()

Out[33]:

  C D
A    
bar 1.182905 3.856180
foo 1.833737 -5.941317

In [34]:

df.groupby(['A','B']).sum()

Out[34]:

    C D
A B    
bar one 1.128497 0.186203
three 1.530352 1.159454
two -1.475944 2.510523
foo one 1.123991 -1.567713
three -0.799283 -2.370425
two 1.509028 -2.003179

重塑

In [35]:

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

Out[35]:

[('bar', 'one'),
 ('bar', 'two'),
 ('baz', 'one'),
 ('baz', 'two'),
 ('foo', 'one'),
 ('foo', 'two'),
 ('qux', 'one'),
 ('qux', 'two')]

In [36]:

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

Out[36]:

MultiIndex(levels=[['bar', 'baz', 'foo', 'qux'], ['one', 'two']],
           labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]],
           names=['first', 'second'])

In [37]:

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

Out[37]:

    A B
first second    
bar one -1.421775 0.156963
two 1.564838 2.088619
baz one 0.371071 0.103863
two -0.789093 0.952786
foo one 1.931205 0.103578
two 0.169767 -0.750791
qux one 0.601066 -0.779490
two 1.416660 1.744881

In [39]:

df2 = df[:4]
df2

Out[39]:

    A B
first second    
bar one -1.421775 0.156963
two 1.564838 2.088619
baz one 0.371071 0.103863
two -0.789093 0.952786

In [40]:

stacked = df2.stack()
stacked

Out[40]:

first  second   
bar    one     A   -1.421775
               B    0.156963
       two     A    1.564838
               B    2.088619
baz    one     A    0.371071
               B    0.103863
       two     A   -0.789093
               B    0.952786
dtype: float64

In [41]:

stacked.unstack()

Out[41]:

    A B
first second    
bar one -1.421775 0.156963
two 1.564838 2.088619
baz one 0.371071 0.103863
two -0.789093 0.952786

In [42]:

stacked.unstack(1)

Out[42]:

  second one two
first      
bar A -1.421775 1.564838
B 0.156963 2.088619
baz A 0.371071 -0.789093
B 0.103863 0.952786

In [43]:

stacked.unstack(0)

Out[43]:

  first bar baz
second      
one A -1.421775 0.371071
B 0.156963 0.103863
two A 1.564838 -0.789093
B 2.088619 0.952786

数据透视表

In [44]:

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)})
df

Out[44]:

  A B C D E
0 one A foo 1.955130 0.720242
1 one B foo 1.603413 1.022816
2 two C foo -0.019881 -0.624567
3 three A bar 1.167609 -1.001875
4 one B bar -0.468257 0.008671
5 one C bar 1.081470 1.286024
6 two A foo -1.302491 0.751544
7 three B foo 1.139926 -0.791500
8 one C foo -2.829628 0.850286
9 one A bar -2.277605 -0.943648
10 two B bar 1.091066 -0.566744
11 three C bar -1.175964 0.731061

In [45]:

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

Out[45]:

  C bar foo
A B    
one A -2.277605 1.955130
B -0.468257 1.603413
C 1.081470 -2.829628
three A 1.167609 NaN
B NaN 1.139926
C -1.175964 NaN
two A NaN -1.302491
B 1.091066 NaN
C NaN -0.019881

时间序列

In [46]:

rng = pd.date_range('1/1/2012', periods=100, freq='S')
rng

Out[46]:

DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:00:01',
               '2012-01-01 00:00:02', '2012-01-01 00:00:03',
               '2012-01-01 00:00:04', '2012-01-01 00:00:05',
               '2012-01-01 00:00:06', '2012-01-01 00:00:07',
               '2012-01-01 00:00:08', '2012-01-01 00:00:09',
               '2012-01-01 00:00:10', '2012-01-01 00:00:11',
               '2012-01-01 00:00:12', '2012-01-01 00:00:13',
               '2012-01-01 00:00:14', '2012-01-01 00:00:15',
               '2012-01-01 00:00:16', '2012-01-01 00:00:17',
               '2012-01-01 00:00:18', '2012-01-01 00:00:19',
               '2012-01-01 00:00:20', '2012-01-01 00:00:21',
               '2012-01-01 00:00:22', '2012-01-01 00:00:23',
               '2012-01-01 00:00:24', '2012-01-01 00:00:25',
               '2012-01-01 00:00:26', '2012-01-01 00:00:27',
               '2012-01-01 00:00:28', '2012-01-01 00:00:29',
               '2012-01-01 00:00:30', '2012-01-01 00:00:31',
               '2012-01-01 00:00:32', '2012-01-01 00:00:33',
               '2012-01-01 00:00:34', '2012-01-01 00:00:35',
               '2012-01-01 00:00:36', '2012-01-01 00:00:37',
               '2012-01-01 00:00:38', '2012-01-01 00:00:39',
               '2012-01-01 00:00:40', '2012-01-01 00:00:41',
               '2012-01-01 00:00:42', '2012-01-01 00:00:43',
               '2012-01-01 00:00:44', '2012-01-01 00:00:45',
               '2012-01-01 00:00:46', '2012-01-01 00:00:47',
               '2012-01-01 00:00:48', '2012-01-01 00:00:49',
               '2012-01-01 00:00:50', '2012-01-01 00:00:51',
               '2012-01-01 00:00:52', '2012-01-01 00:00:53',
               '2012-01-01 00:00:54', '2012-01-01 00:00:55',
               '2012-01-01 00:00:56', '2012-01-01 00:00:57',
               '2012-01-01 00:00:58', '2012-01-01 00:00:59',
               '2012-01-01 00:01:00', '2012-01-01 00:01:01',
               '2012-01-01 00:01:02', '2012-01-01 00:01:03',
               '2012-01-01 00:01:04', '2012-01-01 00:01:05',
               '2012-01-01 00:01:06', '2012-01-01 00:01:07',
               '2012-01-01 00:01:08', '2012-01-01 00:01:09',
               '2012-01-01 00:01:10', '2012-01-01 00:01:11',
               '2012-01-01 00:01:12', '2012-01-01 00:01:13',
               '2012-01-01 00:01:14', '2012-01-01 00:01:15',
               '2012-01-01 00:01:16', '2012-01-01 00:01:17',
               '2012-01-01 00:01:18', '2012-01-01 00:01:19',
               '2012-01-01 00:01:20', '2012-01-01 00:01:21',
               '2012-01-01 00:01:22', '2012-01-01 00:01:23',
               '2012-01-01 00:01:24', '2012-01-01 00:01:25',
               '2012-01-01 00:01:26', '2012-01-01 00:01:27',
               '2012-01-01 00:01:28', '2012-01-01 00:01:29',
               '2012-01-01 00:01:30', '2012-01-01 00:01:31',
               '2012-01-01 00:01:32', '2012-01-01 00:01:33',
               '2012-01-01 00:01:34', '2012-01-01 00:01:35',
               '2012-01-01 00:01:36', '2012-01-01 00:01:37',
               '2012-01-01 00:01:38', '2012-01-01 00:01:39'],
              dtype='datetime64[ns]', freq='S')

In [48]:

ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
ts

Out[48]:

2012-01-01 00:00:00    478
2012-01-01 00:00:01    350
2012-01-01 00:00:02     17
2012-01-01 00:00:03    439
2012-01-01 00:00:04     84
2012-01-01 00:00:05    164
2012-01-01 00:00:06     17
2012-01-01 00:00:07    182
2012-01-01 00:00:08    482
2012-01-01 00:00:09    198
2012-01-01 00:00:10    231
2012-01-01 00:00:11    446
2012-01-01 00:00:12    271
2012-01-01 00:00:13    453
2012-01-01 00:00:14    418
2012-01-01 00:00:15    235
2012-01-01 00:00:16    231
2012-01-01 00:00:17     95
2012-01-01 00:00:18    132
2012-01-01 00:00:19    364
2012-01-01 00:00:20     93
2012-01-01 00:00:21    225
2012-01-01 00:00:22    255
2012-01-01 00:00:23    120
2012-01-01 00:00:24    387
2012-01-01 00:00:25    175
2012-01-01 00:00:26    174
2012-01-01 00:00:27    426
2012-01-01 00:00:28    185
2012-01-01 00:00:29     44
                      ... 
2012-01-01 00:01:10     61
2012-01-01 00:01:11      3
2012-01-01 00:01:12     14
2012-01-01 00:01:13    342
2012-01-01 00:01:14    361
2012-01-01 00:01:15    394
2012-01-01 00:01:16    414
2012-01-01 00:01:17    452
2012-01-01 00:01:18    191
2012-01-01 00:01:19    297
2012-01-01 00:01:20     16
2012-01-01 00:01:21    481
2012-01-01 00:01:22    364
2012-01-01 00:01:23     73
2012-01-01 00:01:24    238
2012-01-01 00:01:25    331
2012-01-01 00:01:26    111
2012-01-01 00:01:27    347
2012-01-01 00:01:28     28
2012-01-01 00:01:29    276
2012-01-01 00:01:30     33
2012-01-01 00:01:31    315
2012-01-01 00:01:32    177
2012-01-01 00:01:33     39
2012-01-01 00:01:34    211
2012-01-01 00:01:35    214
2012-01-01 00:01:36     65
2012-01-01 00:01:37    151
2012-01-01 00:01:38    418
2012-01-01 00:01:39    256
Freq: S, Length: 100, dtype: int32

In [49]:

ts.resample('5Min').sum()

Out[49]:

2012-01-01    25315
Freq: 5T, dtype: int32

In [50]:

rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
ts = pd.Series(np.random.randn(len(rng)), rng)
ts

Out[50]:

2012-03-06   -0.844716
2012-03-07   -0.354952
2012-03-08    0.847330
2012-03-09    0.199636
2012-03-10   -0.157474
Freq: D, dtype: float64

In [51]:

ts_utc = ts.tz_localize('UTC')
ts_utc

Out[51]:

2012-03-06 00:00:00+00:00   -0.844716
2012-03-07 00:00:00+00:00   -0.354952
2012-03-08 00:00:00+00:00    0.847330
2012-03-09 00:00:00+00:00    0.199636
2012-03-10 00:00:00+00:00   -0.157474
Freq: D, dtype: float64

转换为另一个时区：

In [52]:

ts_utc.tz_convert('US/Eastern')

Out[52]:

2012-03-05 19:00:00-05:00   -0.844716
2012-03-06 19:00:00-05:00   -0.354952
2012-03-07 19:00:00-05:00    0.847330
2012-03-08 19:00:00-05:00    0.199636
2012-03-09 19:00:00-05:00   -0.157474
Freq: D, dtype: float64

在时间跨度表示之间转换：

In [53]:

rng = pd.date_range('1/1/2012', periods=5, freq='M')
ts = pd.Series(np.random.randn(len(rng)), index=rng)
ts

Out[53]:

2012-01-31   -0.081224
2012-02-29    1.507458
2012-03-31    0.892793
2012-04-30   -1.667747
2012-05-31    1.100369
Freq: M, dtype: float64

In [55]:

ps = ts.to_period()
ps

Out[55]:

2012-01   -0.081224
2012-02    1.507458
2012-03    0.892793
2012-04   -1.667747
2012-05    1.100369
Freq: M, dtype: float64

In [56]:

ps.to_timestamp()

Out[56]:

2012-01-01   -0.081224
2012-02-01    1.507458
2012-03-01    0.892793
2012-04-01   -1.667747
2012-05-01    1.100369
Freq: MS, dtype: float64

In [57]:

prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
prng

Out[57]:

PeriodIndex(['1990Q1', '1990Q2', '1990Q3', '1990Q4', '1991Q1', '1991Q2',
             '1991Q3', '1991Q4', '1992Q1', '1992Q2', '1992Q3', '1992Q4',
             '1993Q1', '1993Q2', '1993Q3', '1993Q4', '1994Q1', '1994Q2',
             '1994Q3', '1994Q4', '1995Q1', '1995Q2', '1995Q3', '1995Q4',
             '1996Q1', '1996Q2', '1996Q3', '1996Q4', '1997Q1', '1997Q2',
             '1997Q3', '1997Q4', '1998Q1', '1998Q2', '1998Q3', '1998Q4',
             '1999Q1', '1999Q2', '1999Q3', '1999Q4', '2000Q1', '2000Q2',
             '2000Q3', '2000Q4'],
            dtype='period[Q-NOV]', freq='Q-NOV')

In [58]:

ts = pd.Series(np.random.randn(len(prng)), prng)
ts

Out[58]:

1990Q1    1.115171
1990Q2   -0.910567
1990Q3    0.572898
1990Q4    0.122395
1991Q1   -0.742167
1991Q2   -0.249733
1991Q3    0.008649
1991Q4    0.922641
1992Q1   -0.377793
1992Q2   -0.810604
1992Q3    1.347122
1992Q4   -1.089756
1993Q1    0.921470
1993Q2   -0.315505
1993Q3    0.054035
1993Q4    0.196308
1994Q1    0.158628
1994Q2   -1.547737
1994Q3    0.805891
1994Q4   -2.249038
1995Q1    2.174847
1995Q2   -1.205269
1995Q3   -0.251231
1995Q4   -0.810176
1996Q1   -0.044167
1996Q2    0.893497
1996Q3    1.552094
1996Q4   -0.612805
1997Q1    0.918409
1997Q2    0.980157
1997Q3    1.509811
1997Q4    0.059969
1998Q1   -0.359608
1998Q2    1.643402
1998Q3    1.616181
1998Q4    1.882245
1999Q1    0.128783
1999Q2   -0.391579
1999Q3    1.448685
1999Q4    1.829124
2000Q1    1.583030
2000Q2   -1.449266
2000Q3   -0.153701
2000Q4    0.726885
Freq: Q-NOV, dtype: float64

In [61]:

ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
ts.head()

Out[61]:

1990-03-01 09:00    1.115171
1990-06-01 09:00   -0.910567
1990-09-01 09:00    0.572898
1990-12-01 09:00    0.122395
1991-03-01 09:00   -0.742167
Freq: H, dtype: float64

分类

In [63]:

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

Out[63]:

  id raw_grade
0 1 a
1 2 b
2 3 b
3 4 a
4 5 a
5 6 e

In [67]:

df["grade"] = df["raw_grade"].astype("category")
df["grade"]

Out[67]:

0    a
1    b
2    b
3    a
4    a
5    e
Name: grade, dtype: category
Categories (3, object): [a, b, e]

In [66]:

df

Out[66]:

  id raw_grade grade
0 1 a a
1 2 b b
2 3 b b
3 4 a a
4 5 a a
5 6 e e

In [68]:

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

Out[68]:

0    very good
1         good
2         good
3    very good
4    very good
5     very bad
Name: grade, dtype: category
Categories (3, object): [very good, good, very bad]

In [69]:

df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])
df["grade"]

Out[69]:

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]

In [70]:

df

Out[70]:

  id raw_grade grade
0 1 a very good
1 2 b good
2 3 b good
3 4 a very good
4 5 a very good
5 6 e very bad

In [71]:

df.groupby("grade").size()

Out[71]:

grade
very bad     1
bad          0
medium       0
good         2
very good    3
dtype: int64

绘图

In [81]:

%matplotlib  inline

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts
ts = ts.cumsum()
ts
ts.plot()

Out[81]:

<matplotlib.axes._subplots.AxesSubplot at 0x23b41331630>

10分钟 to pandas

In [93]:

import matplotlib.pyplot as plt

df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
                  columns=['A', 'B', 'C', 'D'])
df = df.cumsum()
plt.figure(); df.plot(); plt.legend(loc='best')

Out[93]:

<matplotlib.legend.Legend at 0x23b418382b0>
<matplotlib.figure.Figure at 0x23b414e5b70>

10分钟 to pandas

获取数据进/出

In [94]:

'''
df.to_csv('foo.csv')
pd.read_csv('foo.csv')

df.to_hdf('foo.h5','df')
pd.read_hdf('foo.h5','df')

df.to_excel('foo.xlsx', sheet_name='Sheet1')
pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])
'''

Out[94]:

"\ndf.to_csv('foo.csv')\npd.read_csv('foo.csv')\n\ndf.to_hdf('foo.h5','df')\npd.read_hdf('foo.h5','df')\n\ndf.to_excel('foo.xlsx', sheet_name='Sheet1')\npd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])\n"

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