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[4]
import pandas as pd
import numpy as np
df = pd.DataFrame({
'date':pd.date_range(start='20210714',periods=7,freq='D'),
'a': np.linspace(0,6,7),
'b': np.random.randn(7),
'c': np.random.choice(['Low','Medium','High'],7).tolist(),
'd': np.random.normal(100,10,size=(7)).tolist()
})
df
date a b c d
0 2021-07-14 0.0 -0.079268 Low 100.637433
1 2021-07-15 1.0 0.231418 High 112.083560
2 2021-07-16 2.0 0.288950 Medium 108.132161
3 2021-07-17 3.0 0.264166 High 90.819338
4 2021-07-18 4.0 -0.750558 Medium 100.886340
5 2021-07-19 5.0 1.173738 Medium 104.307198
6 2021-07-20 6.0 -0.418391 Low 88.523432
[6]
# for in 循环的是列
for col in df:
print(col)
print(df[col])
date
0 2021-07-14
1 2021-07-15
2 2021-07-16
3 2021-07-17
4 2021-07-18
5 2021-07-19
6 2021-07-20
Name: date, dtype: datetime64[ns]
a
0 0.0
1 1.0
2 2.0
3 3.0
4 4.0
5 5.0
6 6.0
Name: a, dtype: float64
b
0 -0.079268
1 0.231418
2 0.288950
3 0.264166
4 -0.750558
5 1.173738
6 -0.418391
Name: b, dtype: float64
c
0 Low
1 High
2 Medium
3 High
4 Medium
5 Medium
6 Low
Name: c, dtype: object
d
0 100.637433
1 112.083560
2 108.132161
3 90.819338
4 100.886340
5 104.307198
6 88.523432
Name: d, dtype: float64
[8]
# iteritem 获取列和值
for key,value in df.iteritems():
print(key)
print(value)
date
0 2021-07-14
1 2021-07-15
2 2021-07-16
3 2021-07-17
4 2021-07-18
5 2021-07-19
6 2021-07-20
Name: date, dtype: datetime64[ns]
a
0 0.0
1 1.0
2 2.0
3 3.0
4 4.0
5 5.0
6 6.0
Name: a, dtype: float64
b
0 -0.079268
1 0.231418
2 0.288950
3 0.264166
4 -0.750558
5 1.173738
6 -0.418391
Name: b, dtype: float64
c
0 Low
1 High
2 Medium
3 High
4 Medium
5 Medium
6 Low
Name: c, dtype: object
d
0 100.637433
1 112.083560
2 108.132161
3 90.819338
4 100.886340
5 104.307198
6 88.523432
Name: d, dtype: float64
[9]
# 按行打印，逐行迭代
for key,value in df.iterrows():
print(key)
print(value)
0
date 2021-07-14 00:00:00
a 0
b -0.0792684
c Low
d 100.637
Name: 0, dtype: object
1
date 2021-07-15 00:00:00
a 1
b 0.231418
c High
d 112.084
Name: 1, dtype: object
2
date 2021-07-16 00:00:00
a 2
b 0.28895
c Medium
d 108.132
Name: 2, dtype: object
3
date 2021-07-17 00:00:00
a 3
b 0.264166
c High
d 90.8193
Name: 3, dtype: object
4
date 2021-07-18 00:00:00
a 4
b -0.750558
c Medium
d 100.886
Name: 4, dtype: object
5
date 2021-07-19 00:00:00
a 5
b 1.17374
c Medium
d 104.307
Name: 5, dtype: object
6
date 2021-07-20 00:00:00
a 6
b -0.418391
c Low
d 88.5234
Name: 6, dtype: object
[12]
# 以元组形式打印
for row in df.itertuples():
print(row)
Pandas(Index=0, date=Timestamp('2021-07-14 00:00:00'), a=0.0, b=-0.07926836478101182, c='Low', d=100.6374326023984)
Pandas(Index=1, date=Timestamp('2021-07-15 00:00:00'), a=1.0, b=0.23141819210674755, c='High', d=112.08356043292231)
Pandas(Index=2, date=Timestamp('2021-07-16 00:00:00'), a=2.0, b=0.28895002255434654, c='Medium', d=108.13216066430968)
Pandas(Index=3, date=Timestamp('2021-07-17 00:00:00'), a=3.0, b=0.26416569787454686, c='High', d=90.81933760723473)
Pandas(Index=4, date=Timestamp('2021-07-18 00:00:00'), a=4.0, b=-0.7505580643324384, c='Medium', d=100.88634049762355)
Pandas(Index=5, date=Timestamp('2021-07-19 00:00:00'), a=5.0, b=1.1737384361425682, c='Medium', d=104.30719772518808)
Pandas(Index=6, date=Timestamp('2021-07-20 00:00:00'), a=6.0, b=-0.41839064630765915, c='Low', d=88.52343226534083)
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