df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
print(df)
print('-'*50)
print(df.loc['viper']) # 返回的为serial
print(df.loc[['viper']]) # 返回的为DateFrame
print(df.loc['cobra', 'shield']) # 返回的是一个label
print(df.loc['cobra':'viper', 'max_speed']) # 按索引切片
print(df.loc[[False, False, True]]) # 与row相同的boolean 列表
print(df.loc[pd.Series([False, True, False],
index=['viper', 'sidewinder', 'cobra'])])
print(df.loc[pd.Index(["cobra", "viper"], name="foo")]) # 重置索引
print(df[df['shield'] > 6]) # print(df.loc[df['shield'] > 6]) 这俩的结果相等
print(df.loc[df['shield'] > 2,['max_speed']])
df.loc[['viper', 'sidewinder'], ['shield']] = 50 # 设置一个label的值
df.loc['cobra'] = 10 # 设置一行的值
df.loc[:,'max_speed'] = 30 # 设置一列的值
df.max_speed = 30 # 设置一列的值 根据column的值
df.loc[df['shield'] > 35] = 0 # df[df['shield'] > 35] = 0 根据条件设置行的值
# print(df)
tuples = [
('cobra', 'mark i'), ('cobra', 'mark ii'),
('sidewinder', 'mark i'), ('sidewinder', 'mark ii'),
('viper', 'mark ii'), ('viper', 'mark iii')
]
index = pd.MultiIndex.from_tuples(tuples)
values = [[12, 2], [0, 4], [10, 20],
[1, 4], [7, 1], [16, 36]]
df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
print(df)
print(df.loc['cobra'])
print(df.loc[('cobra', 'mark ii')])
print(df.loc[[('cobra', 'mark ii')]])
print(df.loc[('cobra', 'mark i'):'sidewinder'])
print(df.loc[('cobra', 'mark i'):('viper', 'mark ii')])
