Python常見的pandas用法demo示例
本文實(shí)例總結(jié)了Python常見的pandas用法。分享給大家供大家參考，具體如下：
import numpy as np import pandas as pd
s = pd.Series([1,3,6, np.nan, 44, 1]) #定義一個(gè)序列。 序列就是一列內(nèi)容，每一行有一個(gè)index值 print(s) print(s.index)
0 1.0
1 3.0
2 6.0
3 NaN
4 44.0
5 1.0
dtype: float64
RangeIndex(start=0, stop=6, step=1)
dates = pd.date_range('20180101', periods=6)
print(dates)
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
'2018-01-05', '2018-01-06'],
dtype='datetime64[ns]', freq='D')
df1 = pd.DataFrame(np.arange(12).reshape(3,4)) #定義DataFrame，可以看作一個(gè)有index和colunms的矩陣 print(df)
0 1 2 3
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
df2 = pd.DataFrame(np.random.randn(6,4), index=dates, columns=['a', 'b', 'c', 'd']) #np.random.randn(6,4)生成6行4列矩陣 print(df)
a b c d
2018-01-01 0.300675 1.769383 1.244406 -1.058294
2018-01-02 0.832666 2.216755 0.178716 -0.156828
2018-01-03 1.314190 -0.866199 0.836150 1.001026
2018-01-04 -1.671724 1.147406 -0.148676 -0.272555
2018-01-05 1.146664 2.022861 -1.833995 -0.627568
2018-01-06 -0.192242 1.517676 0.756707 0.058869
df = pd.DataFrame({'A':1.0,
'B':pd.Timestamp('20180101'),
'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'}) #按照給出的逐列定義df
print(df)
print(df.dtypes)
A B C D E F
0 1.0 2018-01-01 1.0 3 test foo
1 1.0 2018-01-01 1.0 3 train foo
2 1.0 2018-01-01 1.0 3 test foo
3 1.0 2018-01-01 1.0 3 train foo
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object
#df的行、列、值 print(df.index) print(df.columns) print(df.values)
Int64Index([0, 1, 2, 3], dtype='int64')
Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')
[[1.0 Timestamp('2018-01-01 00:00:00') 1.0 3 'test' 'foo']
[1.0 Timestamp('2018-01-01 00:00:00') 1.0 3 'train' 'foo']
[1.0 Timestamp('2018-01-01 00:00:00') 1.0 3 'test' 'foo']
[1.0 Timestamp('2018-01-01 00:00:00') 1.0 3 'train' 'foo']]
print(df.describe()) #統(tǒng)計(jì) print(df.T) #轉(zhuǎn)置
A C D
count 4.0 4.0 4.0
mean 1.0 1.0 3.0
std 0.0 0.0 0.0
min 1.0 1.0 3.0
25% 1.0 1.0 3.0
50% 1.0 1.0 3.0
75% 1.0 1.0 3.0
max 1.0 1.0 3.0
0 1 2 \
A 1 1 1
B 2018-01-01 00:00:00 2018-01-01 00:00:00 2018-01-01 00:00:00
C 1 1 1
D 3 3 3
E test train test
F foo foo foo
3
A 1
B 2018-01-01 00:00:00
C 1
D 3
E train
F foo
#df排序 print(df.sort_index(axis=1, ascending=False)) #根據(jù)索引值對各行進(jìn)行排序（相當(dāng)于重新排列各列的位置） print(df.sort_values(by='E')) #根據(jù)內(nèi)容值對各列進(jìn)行排序
F E D C B A
0 foo test 3 1.0 2018-01-01 1.0
1 foo train 3 1.0 2018-01-01 1.0
2 foo test 3 1.0 2018-01-01 1.0
3 foo train 3 1.0 2018-01-01 1.0
A B C D E F
0 1.0 2018-01-01 1.0 3 test foo
2 1.0 2018-01-01 1.0 3 test foo
1 1.0 2018-01-01 1.0 3 train foo
3 1.0 2018-01-01 1.0 3 train foo
indexes = pd.date_range('20180101', periods=6)
df3 = pd.DataFrame(np.arange(24).reshape(6, 4), index=indexes, columns=['A', 'B', 'C', 'D'])
print(df3)
print()
#選擇column
print(df3['A'])
print()
print(df3.A)
A B C D
2018-01-01 0 1 2 3
2018-01-02 4 5 6 7
2018-01-03 8 9 10 11
2018-01-04 12 13 14 15
2018-01-05 16 17 18 19
2018-01-06 20 21 22 23
2018-01-01 0
2018-01-02 4
2018-01-03 8
2018-01-04 12
2018-01-05 16
2018-01-06 20
Freq: D, Name: A, dtype: int32
2018-01-01 0
2018-01-02 4
2018-01-03 8
2018-01-04 12
2018-01-05 16
2018-01-06 20
Freq: D, Name: A, dtype: int32
A B C D
2018-01-01 0 1 2 3
2018-01-02 4 5 6 7
2018-01-03 8 9 10 11
#選擇行， 類似limit語句 print(df3[0:0]) print() print(df3[0:3]) print() print(df3['20180103':'20180105'])
Empty DataFrame
Columns: [A, B, C, D]
Index: []
A B C D
2018-01-01 0 1 2 3
2018-01-02 4 5 6 7
2018-01-03 8 9 10 11
A B C D
2018-01-03 8 9 10 11
2018-01-04 12 13 14 15
2018-01-05 16 17 18 19
print(df3.loc['20180102']) #返回指定行構(gòu)成的序列
A 4
B 5
C 6
D 7
Name: 2018-01-02 00:00:00, dtype: int32
print(df3.loc['20180103', ['A','C']]) #列篩選 print() print(df3.loc['20180103':'20180105', ['A','C']]) #子df，類似select A, C from df limit ... print() print(df3.loc[:, ['A', 'B']])
A 8
C 10
Name: 2018-01-03 00:00:00, dtype: int32
A C
2018-01-03 8 10
2018-01-04 12 14
2018-01-05 16 18
A B
2018-01-01 0 1
2018-01-02 4 5
2018-01-03 8 9
2018-01-04 12 13
2018-01-05 16 17
2018-01-06 20 21
print(df3);print() print(df3.iloc[1]);print() print(df3.iloc[1,1]);print() print(df3.iloc[:,1]);print() print(df3.iloc[0:3,1:3]);print() print(df3.iloc[[1,3,5],[0,2]]) #行可以不連續(xù)，limit做不到
A B C D
2018-01-01 0 1 2 3
2018-01-02 4 5 6 7
2018-01-03 8 9 10 11
2018-01-04 12 13 14 15
2018-01-05 16 17 18 19
2018-01-06 20 21 22 23
A 4
B 5
C 6
D 7
Name: 2018-01-02 00:00:00, dtype: int32
5
2018-01-01 1
2018-01-02 5
2018-01-03 9
2018-01-04 13
2018-01-05 17
2018-01-06 21
Freq: D, Name: B, dtype: int32
B C
2018-01-01 1 2
2018-01-02 5 6
2018-01-03 9 10
A C
2018-01-02 4 6
2018-01-04 12 14
2018-01-06 20 22
# print(df3.ix[:3, ['A', 'C']])\ print(df3);print() print(df3[df3.A >= 8]) #根據(jù)值進(jìn)行條件過濾，類似where A >= 8條件語句
A B C D
2018-01-01 0 1 2 3
2018-01-02 4 5 6 7
2018-01-03 8 9 10 11
2018-01-04 12 13 14 15
2018-01-05 16 17 18 19
2018-01-06 20 21 22 23
A B C D
2018-01-03 8 9 10 11
2018-01-04 12 13 14 15
2018-01-05 16 17 18 19
2018-01-06 20 21 22 23
indexes1 = pd.date_range('20180101', periods=6)
df4 = pd.DataFrame(np.arange(24).reshape(6, 4), index=indexes1, columns=['A', 'B', 'C', 'D'])
print(df4);print()
#給某個(gè)元素賦值
df4.A[1] = 1111
df4.B['20180103'] = 2222
df4.iloc[3, 2] = 3333
df4.loc['20180105', 'D'] = 4444
print(df4);print()
#范圍賦值
df4.B[df4.A < 10] = -1
print(df4);print()
df4[df4.A < 10] = 0
print(df4);print()
A B C D
2018-01-01 0 1 2 3
2018-01-02 4 5 6 7
2018-01-03 8 9 10 11
2018-01-04 12 13 14 15
2018-01-05 16 17 18 19
2018-01-06 20 21 22 23
A B C D
2018-01-01 0 1 2 3
2018-01-02 1111 5 6 7
2018-01-03 8 2222 10 11
2018-01-04 12 13 3333 15
2018-01-05 16 17 18 4444
2018-01-06 20 21 22 23
A B C D
2018-01-01 0 -1 2 3
2018-01-02 1111 5 6 7
2018-01-03 8 -1 10 11
2018-01-04 12 13 3333 15
2018-01-05 16 17 18 4444
2018-01-06 20 21 22 23
A B C D
2018-01-01 0 0 0 0
2018-01-02 1111 5 6 7
2018-01-03 0 0 0 0
2018-01-04 12 13 3333 15
2018-01-05 16 17 18 4444
2018-01-06 20 21 22 23
indexes1 = pd.date_range('20180101', periods=6)
df4 = pd.DataFrame(np.arange(24).reshape(6, 4), index=indexes1, columns=['A', 'B', 'C', 'D'])
print(df4);print()
#添加一列
df4['E'] = np.NaN
print(df4);print()
#由于index沒對齊，原df沒有的行默認(rèn)為NaN，類型為float64,多出的行丟棄
df4['F'] = pd.Series([1,2,3,4,5,6], index=pd.date_range('20180102', periods=6))
print(df4);print()
print(df4.dtypes)
A B C D
2018-01-01 0 1 2 3
2018-01-02 4 5 6 7
2018-01-03 8 9 10 11
2018-01-04 12 13 14 15
2018-01-05 16 17 18 19
2018-01-06 20 21 22 23
A B C D E
2018-01-01 0 1 2 3 NaN
2018-01-02 4 5 6 7 NaN
2018-01-03 8 9 10 11 NaN
2018-01-04 12 13 14 15 NaN
2018-01-05 16 17 18 19 NaN
2018-01-06 20 21 22 23 NaN
A B C D E F
2018-01-01 0 1 2 3 NaN NaN
2018-01-02 4 5 6 7 NaN 1.0
2018-01-03 8 9 10 11 NaN 2.0
2018-01-04 12 13 14 15 NaN 3.0
2018-01-05 16 17 18 19 NaN 4.0
2018-01-06 20 21 22 23 NaN 5.0
A int32
B int32
C int32
D int32
E float64
F float64
dtype: object
df_t = pd.DataFrame(np.arange(24).reshape(6, 4), index=[1,2,3,4,5,6], columns=['A', 'B', 'C', 'D']) df_t.iloc[0, 1] = np.NaN df_t.iloc[1, 2] = np.NaN df = df_t.copy() print(df);print() print(df.dropna(axis=0, how='any'));print() df = df_t.copy() print(df.dropna(axis=1, how='any'));print() df = df_t.copy() df.C = np.NaN print(df);print() print(df.dropna(axis=1, how='all'));print()
A B C D
1 0 NaN 2.0 3
2 4 5.0 NaN 7
3 8 9.0 10.0 11
4 12 13.0 14.0 15
5 16 17.0 18.0 19
6 20 21.0 22.0 23
A B C D
3 8 9.0 10.0 11
4 12 13.0 14.0 15
5 16 17.0 18.0 19
6 20 21.0 22.0 23
A D
1 0 3
2 4 7
3 8 11
4 12 15
5 16 19
6 20 23
A B C D
1 0 NaN NaN 3
2 4 5.0 NaN 7
3 8 9.0 NaN 11
4 12 13.0 NaN 15
5 16 17.0 NaN 19
6 20 21.0 NaN 23
A B D
1 0 NaN 3
2 4 5.0 7
3 8 9.0 11
4 12 13.0 15
5 16 17.0 19
6 20 21.0 23
df = df_t.copy() print(df);print() print(df.isna());print() print(df.isnull().any());print() #isnull是isna別名，功能一樣 print(df.isnull().any(axis=1));print() print(np.any(df.isna() == True));print() print(df.fillna(value=0)) #將NaN賦值
A B C D
1 0 NaN 2.0 3
2 4 5.0 NaN 7
3 8 9.0 10.0 11
4 12 13.0 14.0 15
5 16 17.0 18.0 19
6 20 21.0 22.0 23
A B C D
1 False True False False
2 False False True False
3 False False False False
4 False False False False
5 False False False False
6 False False False False
A False
B True
C True
D False
dtype: bool
1 True
2 True
3 False
4 False
5 False
6 False
dtype: bool
True
A B C D
1 0 0.0 2.0 3
2 4 5.0 0.0 7
3 8 9.0 10.0 11
4 12 13.0 14.0 15
5 16 17.0 18.0 19
6 20 21.0 22.0 23
data = pd.read_csv('D:/pythonwp/test/student.csv')
print(data)
data.to_pickle('D:/pythonwp/test/student.pickle')
id name age gender
0 1 牛帥 23 Male
1 2 gyb 89 Male
2 3 xxs 27 Male
3 4 hey 24 Female
4 5 奧萊利赫本 66 Female
5 6 Jackson 61 Male
6 7 牛帥 23 Male
df0 = pd.DataFrame(np.ones((3, 4)) * 0, columns=['A', 'B', 'C', 'D']) df1 = pd.DataFrame(np.ones((3, 4)) * 1, columns=['A', 'B', 'C', 'D']) df2 = pd.DataFrame(np.ones((3, 4)) * 2, columns=['A', 'B', 'C', 'D']) print(df0); print() print(df1); print() print(df2); print() res = pd.concat([df0, df1, df2], axis = 0) print(res); print() res = pd.concat([df0, df1, df2], axis = 0, ignore_index=True) print(res)
A B C D
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
A B C D
0 1.0 1.0 1.0 1.0
1 1.0 1.0 1.0 1.0
2 1.0 1.0 1.0 1.0
A B C D
0 2.0 2.0 2.0 2.0
1 2.0 2.0 2.0 2.0
2 2.0 2.0 2.0 2.0
A B C D
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
0 1.0 1.0 1.0 1.0
1 1.0 1.0 1.0 1.0
2 1.0 1.0 1.0 1.0
0 2.0 2.0 2.0 2.0
1 2.0 2.0 2.0 2.0
2 2.0 2.0 2.0 2.0
A B C D
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
5 1.0 1.0 1.0 1.0
6 2.0 2.0 2.0 2.0
7 2.0 2.0 2.0 2.0
8 2.0 2.0 2.0 2.0
df0 = pd.DataFrame(np.ones((3, 4)) * 0, columns=['A', 'B', 'C', 'D']) df1 = pd.DataFrame(np.ones((3, 4)) * 1, columns=['E', 'F', 'C', 'D']) res = pd.concat([df0, df1], ignore_index=True) print(res);print() res = pd.concat([df0, df1], join='outer', ignore_index=True) print(res);print() res = pd.concat([df0, df1], join='inner',ignore_index=True) print(res);print()
A B C D E F
0 0.0 0.0 0.0 0.0 NaN NaN
1 0.0 0.0 0.0 0.0 NaN NaN
2 0.0 0.0 0.0 0.0 NaN NaN
3 NaN NaN 1.0 1.0 1.0 1.0
4 NaN NaN 1.0 1.0 1.0 1.0
5 NaN NaN 1.0 1.0 1.0 1.0
A B C D E F
0 0.0 0.0 0.0 0.0 NaN NaN
1 0.0 0.0 0.0 0.0 NaN NaN
2 0.0 0.0 0.0 0.0 NaN NaN
3 NaN NaN 1.0 1.0 1.0 1.0
4 NaN NaN 1.0 1.0 1.0 1.0
5 NaN NaN 1.0 1.0 1.0 1.0
C D
0 0.0 0.0
1 0.0 0.0
2 0.0 0.0
3 1.0 1.0
4 1.0 1.0
5 1.0 1.0
#橫向合并 df0 = pd.DataFrame(np.ones((3, 4)) * 0, index=['1', '2', '3'], columns=['A', 'B', 'C', 'D']) df1 = pd.DataFrame(np.ones((3, 4)) * 1, index=['2', '3', '4'], columns=['A', 'B', 'C', 'D']) print(df0);print() print(df1);print() res = pd.concat([df0, df1], axis=1) print(res);print() res = pd.concat([df0, df1], axis=1, join='inner', ignore_index=True) print(res);print() res = pd.concat([df0, df1], axis=1, join_axes=[df0.index]) print(res);print()
A B C D
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0
A B C D
2 1.0 1.0 1.0 1.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
A B C D A B C D
1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN
2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
4 NaN NaN NaN NaN 1.0 1.0 1.0 1.0
0 1 2 3 4 5 6 7
2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
A B C D A B C D
1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN
2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
df0 = pd.DataFrame(np.ones((3, 4)) * 0, index=['1', '2', '3'], columns=['A', 'B', 'C', 'D']) df1 = pd.DataFrame(np.ones((3, 4)) * 1, index=['2', '3', '4'], columns=['A', 'B', 'C', 'D']) print(df0);print() print(df1);print() res = df0.append([df1, df1], ignore_index=False) print(res);print() s = pd.Series([1,2,3,4], index=['A','B','C','E']) print(df0.append(s, ignore_index=True))
A B C D
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0
A B C D
2 1.0 1.0 1.0 1.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
A B C D
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0
2 1.0 1.0 1.0 1.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
2 1.0 1.0 1.0 1.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
A B C D E
0 0.0 0.0 0.0 0.0 NaN
1 0.0 0.0 0.0 0.0 NaN
2 0.0 0.0 0.0 0.0 NaN
3 1.0 2.0 3.0 NaN 4.0
df1 = pd.DataFrame({'key':['K0', 'K1', 'K2'],
'A':['A0', 'A1', 'A2'],
'B':['B0', 'B1', 'B2']})
df2 = pd.DataFrame({'key':['K3', 'K1', 'K2'],
'C':['C3', 'C1', 'C2'],
'D':['D3', 'D1', 'D2']})
print(df1); print()
print(df2); print()
res = pd.merge(df1, df2, on='key')
print(res); print()
res = pd.merge(df1, df2, on='key', how='outer')
print(res); print()
res = pd.merge(df1, df2, on='key', how='left')
print(res); print()
res = pd.merge(df1, df2, on='key', how='right')
print(res); print()
A B key
0 A0 B0 K0
1 A1 B1 K1
2 A2 B2 K2
C D key
0 C3 D3 K3
1 C1 D1 K1
2 C2 D2 K2
A B key C D
0 A1 B1 K1 C1 D1
1 A2 B2 K2 C2 D2
A B key C D
0 A0 B0 K0 NaN NaN
1 A1 B1 K1 C1 D1
2 A2 B2 K2 C2 D2
3 NaN NaN K3 C3 D3
A B key C D
0 A0 B0 K0 NaN NaN
1 A1 B1 K1 C1 D1
2 A2 B2 K2 C2 D2
A B key C D
0 A1 B1 K1 C1 D1
1 A2 B2 K2 C2 D2
2 NaN NaN K3 C3 D3
df1 = pd.DataFrame({'key1':['K0', 'K0', 'K1'],
'key2':['K0', 'K1', 'K1'],
'A':['A0', 'A1', 'A2'],
'B':['B0', 'B1', 'B2']})
df2 = pd.DataFrame({'key1':['K0', 'K0', 'K1', 'K2'],
'key2':['K0', 'K0', 'K1', 'K2'],
'C':['C3', 'C1', 'C2', 'C4'],
'D':['D3', 'D1', 'D2', 'D4']})
print(df1); print()
print(df2); print()
res = pd.merge(df1, df2, on=['key1','key2'])
print(res); print()
res = pd.merge(df1, df2, on=['key1','key2'], how='outer', indicator='indi')
print(res); print()
A B key1 key2
0 A0 B0 K0 K0
1 A1 B1 K0 K1
2 A2 B2 K1 K1
C D key1 key2
0 C3 D3 K0 K0
1 C1 D1 K0 K0
2 C2 D2 K1 K1
3 C4 D4 K2 K2
A B key1 key2 C D
0 A0 B0 K0 K0 C3 D3
1 A0 B0 K0 K0 C1 D1
2 A2 B2 K1 K1 C2 D2
A B key1 key2 C D indi
0 A0 B0 K0 K0 C3 D3 both
1 A0 B0 K0 K0 C1 D1 both
2 A1 B1 K0 K1 NaN NaN left_only
3 A2 B2 K1 K1 C2 D2 both
4 NaN NaN K2 K2 C4 D4 right_only
#以上是根據(jù)值合并。下面根據(jù)index合并
df1 = pd.DataFrame({'A':['A0', 'A1', 'A2'],
'B':['B0', 'B1', 'B2']},
index=['index0', 'index1', 'index2'])
df2 = pd.DataFrame({'A':['C3', 'C1', 'C2'],
'D':['D3', 'D1', 'D2']},
index=['index3', 'index1', 'index2'])
print(df1); print()
print(df2); print()
res = pd.merge(df1, df2, left_index=True, right_index=True)
print(res); print()
res = pd.merge(df1, df2, left_index=True, right_index=True, how='outer', suffixes=['_b', '_g'])
print(res); print()
A B
index0 A0 B0
index1 A1 B1
index2 A2 B2
A D
index3 C3 D3
index1 C1 D1
index2 C2 D2
A_x B A_y D
index1 A1 B1 C1 D1
index2 A2 B2 C2 D2
A_b B A_g D
index0 A0 B0 NaN NaN
index1 A1 B1 C1 D1
index2 A2 B2 C2 D2
index3 NaN NaN C3 D3
res = df1.join(df2, how='outer', lsuffix='_left', rsuffix='_right') #不用on默認(rèn)用索引合并 print(res);print() res = df1.join(df2, on='B', how='outer', lsuffix='_left', rsuffix='_right') #用on指定df1的某列和df2的索引合并 print(res);print()
A_left B A_right D
index0 A0 B0 NaN NaN
index1 A1 B1 C1 D1
index2 A2 B2 C2 D2
index3 NaN NaN C3 D3
A_left B A_right D
index0 A0 B0 NaN NaN
index1 A1 B1 NaN NaN
index2 A2 B2 NaN NaN
index2 NaN index3 C3 D3
index2 NaN index1 C1 D1
index2 NaN index2 C2 D2
import numpy as np import pandas as pd import matplotlib.pyplot as plt #畫圖模塊 s = pd.Series(np.random.randn(1000), index=np.arange(1000)) s = s.cumsum() #須在命令行執(zhí)行， jupyter會報(bào)錯(cuò) #s.plot() #plt.show() df = pd.DataFrame(np.random.randn(1000, 3), columns=['A', 'B', 'C']) df = df.cumsum() print(df.head()); print() #head默認(rèn)顯示前5行 #須在命令行執(zhí)行， jupyter會報(bào)錯(cuò) #s.plot() #plt.show() #須在命令行執(zhí)行， jupyter會報(bào)錯(cuò) #'bar', 'hist', 'box', 'kde', 'area', 'scatter', 'hexbin', 'pie'... #class_B = df.plot.scatter(x='A', y='B', color='DarkBlue', label='Class B') #畫圖，scatter<散點(diǎn)圖> #df.plot.scatter(x='A', y='C', color='DarkRed', label='Class C', class_B=class_B) #plt.show()
A B C
0 -0.399363 -1.004210 0.641141
1 -1.970009 -0.608482 -0.758504
2 -3.081640 -0.617352 -1.143872
3 -2.174627 -1.383785 -1.011411
4 -1.415515 -1.892226 -2.511739
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