取數
通過位置選擇
import pandas as pd
import numpy as np
dates = pd.date_range('20190101',periods=6)
df=pd.dataframe(np.random.randn(6,4),index=dates,columns=list('abcd'))
print(df)
print(df.iloc[3]) //讀取第三行
a b c d
2019-01-01 -0.612854 -0.253183 -1.373708 -2.301114
2019-01-02 0.212331 -0.609568 -0.392484 0.825583
2019-01-03 1.840755 0.027806 1.224755 -0.624368
2019-01-04 1.036859 -1.144750 1.551773 0.457958
2019-01-05 1.965845 -0.997880 -0.845364 0.012153
2019-01-06 -1.605254 1.835839 0.280078 1.448922
a 1.036859
b -1.144750
c 1.551773
d 0.457958
name: 2019-01-04 00:00:00, dtype: float64
整數切片
print(df.iloc[3:5,0:2])a b2019-01-04 1.036859 -1.14475
2019-01-05 1.965845 -0.99788
print(df.iloc[[1,2,4],[0,2]])a c2019-01-02 0.212331 -0.392484
2019-01-03 1.840755 1.224755
2019-01-05 1.965845 -0.845364
行的切片
print(df.iloc[1:3,:])a b c d
2019-01-02 0.212331 -0.609568 -0.392484 0.825583
2019-01-03 1.840755 0.027806 1.224755 -0.624368
列的切片
print(df.iloc[:,1:3])b c2019-01-01 -0.253183 -1.373708
2019-01-02 -0.609568 -0.392484
2019-01-03 0.027806 1.224755
2019-01-04 -1.144750 1.551773
2019-01-05 -0.997880 -0.845364
2019-01-06 1.835839 0.280078
獲取明確值
print(df.iloc[1,1])
print(df.iat[1,1]) \\結果一樣
-0.6095676716857732
根據單列的值來選擇資料
print(df[df.a>0])a b c d
2019-01-02 0.212331 -0.609568 -0.392484 0.825583
2019-01-03 1.840755 0.027806 1.224755 -0.624368
2019-01-04 1.036859 -1.144750 1.551773 0.457958
2019-01-05 1.965845 -0.997880 -0.845364 0.012153
pycharm炸了，重啟
dfa b c d
2019-01-01 0.913584 -0.147238 -0.146727 0.642962
2019-01-02 -0.193907 1.500496 0.536562 0.536783
2019-01-03 0.773896 -1.628390 -0.299604 0.682026
2019-01-04 -0.107013 1.124117 1.049309 0.296538
2019-01-05 -1.194853 1.492495 -0.147292 -0.313903
2019-01-06 -0.658363 -0.209820 -0.126511 -0.902405
從滿足條件的dataframe中選擇值
print(df[df>0])a b c d
2019-01-01 0.913584 nan nan 0.642962
2019-01-02 nan 1.500496 0.536562 0.536783
2019-01-03 0.773896 nan nan 0.682026
2019-01-04 nan 1.124117 1.049309 0.296538
2019-01-05 nan 1.492495 nan nan
2019-01-06 nan nan nan nan
isin()方法進行過濾
df2=df.copy()
df2['e']=['one','one','two','three','four','three']
print(df2)
print(df2['e'].isin(['two','four']))
a b c d e
2019-01-01 0.913584 -0.147238 -0.146727 0.642962 one
2019-01-02 -0.193907 1.500496 0.536562 0.536783 one
2019-01-03 0.773896 -1.628390 -0.299604 0.682026 two
2019-01-04 -0.107013 1.124117 1.049309 0.296538 three
2019-01-05 -1.194853 1.492495 -0.147292 -0.313903 four
2019-01-06 -0.658363 -0.209820 -0.126511 -0.902405 three
2019-01-01 false
2019-01-02 false
2019-01-03 true
2019-01-04 false
2019-01-05 true
2019-01-06 false
freq: d, name: e, dtype: bool
向現有資料增加乙個新列
import pandas as pd
d=df=pd.dataframe(d)
print('adding a new column by passing as series:')
df['three']=pd.series([10,20,30],index=['a','b','c'])
print(df)
print('adding a new column using the existing columns in dataframe:')
df['four']=df['one']+df['three']
print(df)
adding a new column by passing as series:
one two three
a 1.0 1 10.0
b 2.0 2 20.0
c 3.0 3 30.0
d nan 4 nan
adding a new column using the existing columns in dataframe:
one two three four
a 1.0 1 10.0 11.0
b 2.0 2 20.0 22.0
c 3.0 3 30.0 33.0
d nan 4 nan nan
刪除列
import pandas as pd
d=df=pd.dataframe(d)
print('our dataframe is:')
print(df)
print('deleting the first column using del functions:')
del df['one']
print(df)
print('deleting another column using pop function:')
df.pop('two')
print(df)
our dataframe is:
one two three
a 1.0 1 10.0
b 2.0 2 20.0
c 3.0 3 30.0
d nan 4 nan
deleting the first column using del functions:
two three
a 1 10.0
b 2 20.0
c 3 30.0
d 4 nan
deleting another column using pop function:
three
a 10.0
b 20.0
c 30.0
d nan
附加行34：23
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