Pandas DataFrame 筛选数据几种方法实现

import pandas as pd
import numpy as np

df=pd.DataFrame(np.random.randint(0,150,size=(150,3)),
                columns=['math','python','english']
                )
print(df)
print(df.python)
print(df["math"])
print(df[['math']])
print(df[['math','python']])

import pandas as pd
import numpy as np

df=pd.DataFrame(np.random.randint(0,100,size=(10,3)),
                index=list('ABCDEFGHIG'),
                columns=['math','python','english']
                )
print(df.loc['A'])#筛选出标签A这一行
print(df.loc[['A']])#将A这一行返回成一个新的DataFrame
print(df.loc[['A','B']])#将AB两行返回成DataFrame
print(df.loc[['A','B'],['math','python']])#左边为标签，右边为字段，即将AB两行的math、python两列返回
print(df.loc['A':'F',['math','python']])#采用分区方法，将A到F行的此两列返回
print(df.loc[:,['math','python']])#单独的‘:'表示所有行
print(df.loc['B'::2])#从B开始隔一个取一个

import pandas as pd
import numpy as np

df=pd.DataFrame(np.random.randint(0,151,size=(2000,3)),
                columns=['math','python','english']
                )
cond1=df.python>99#其返回值为一个bool类型的series，需要变量进行接受
print(cond1)
df1=df[cond1]
print(df1[['python']])#将cond1作为筛选条件，这里为了只看其中一列，用一个新的dataframe

print(df[df>50])#对df中的所有元素进行判定，其false项会换成NaN

print(df[df.python.isin([108])])#使用isin方法查找python分数为108，中括号里也可以为数组

df2=df.query('python>135 and math>120 and english > 100')
print(df2)