import pandas as pd
df = pd.read_csv('./data/22/sample_pandas_normal.csv')
print(df)
# name age state point
# 0 Alice 24 NY 64
# 1 Bob 42 CA 92
# 2 Charlie 18 CA 70
# 3 Dave 68 TX 70
# 4 Ellen 24 CA 88
# 5 Frank 30 NY 57
set_index（）的使用方法
在第一个参数键中指定用作索引的列的列名（列标签）。指定的列设置为索引。
df_i = df.set_index('name')
print(df_i)
# age state point
# name
# Alice 24 NY 64
# Bob 42 CA 92
# Charlie 18 CA 70
# Dave 68 TX 70
# Ellen 24 CA 88
# Frank 30 NY 57
df_id = df.set_index('name', drop=False)
print(df_id)
# name age state point
# name
# Alice Alice 24 NY 64
# Bob Bob 42 CA 92
# Charlie Charlie 18 CA 70
# Dave Dave 68 TX 70
# Ellen Ellen 24 CA 88
# Frank Frank 30 NY 57
分配多索引
如果在第一个参数键中指定了列名列表（列标签），则将多列分配为多索引。
df_mi = df.set_index(['state', 'name'])
print(df_mi)
# age point
# state name
# NY Alice 24 64
# CA Bob 42 92
# Charlie 18 70
# TX Dave 68 70
# CA Ellen 24 88
# NY Frank 30 57
使用sort_index（）排序时，它可以整齐显示。
df_mi.sort_index(inplace=True)
print(df_mi)
# age point
# state name
# CA Bob 42 92
# Charlie 18 70
# Ellen 24 88
# NY Alice 24 64
# Frank 30 57
# TX Dave 68 70
print(df_i)
# age state point
# name
# Alice 24 NY 64
# Bob 42 CA 92
# Charlie 18 CA 70
# Dave 68 TX 70
# Ellen 24 CA 88
# Frank 30 NY 57
df_ii = df_i.set_index('state')
print(df_ii)
# age point
# state
# NY 24 64
# CA 42 92
# CA 18 70
# TX 68 70
# CA 24 88
# NY 30 57
如果将参数append设置为True，则除了原始索引之外，还将将指定的列添加为新的层次结构索引。
df_mi = df_i.set_index('state', append=True)
print(df_mi)
# age point
# name state
# Alice NY 24 64
# Bob CA 42 92
# Charlie CA 18 70
# Dave TX 68 70
# Ellen CA 24 88
# Frank NY 30 57
添加的列是最底层。使用swaplevel（）切换图层。
print(df_mi.swaplevel(0, 1))
# age point
# state name
# NY Alice 24 64
# CA Bob 42 92
# Charlie 18 70
# TX Dave 68 70
# CA Ellen 24 88
# NY Frank 30 57
将索引更改为另一列（重置）
与前面的示例一样，如果使用set_index（）指定列，则原始索引将被删除。
如果要保留原始索引，请使用reset_index（），它会从0开始按顺序对索引重新编号。
print(df_i)
# age state point
# name
# Alice 24 NY 64
# Bob 42 CA 92
# Charlie 18 CA 70
# Dave 68 TX 70
# Ellen 24 CA 88
# Frank 30 NY 57
df_ri = df_i.reset_index()
print(df_ri)
# name age state point
# 0 Alice 24 NY 64
# 1 Bob 42 CA 92
# 2 Charlie 18 CA 70
# 3 Dave 68 TX 70
# 4 Ellen 24 CA 88
# 5 Frank 30 NY 57
df_change = df_i.reset_index().set_index('state')
print(df_change)
# name age point
# state
# NY Alice 24 64
# CA Bob 42 92
# CA Charlie 18 70
# TX Dave 68 70
# CA Ellen 24 88
# NY Frank 30 57
df.set_index('name', inplace=True)
print(df)
# age state point
# name
# Alice 24 NY 64
# Bob 42 CA 92
# Charlie 18 CA 70
# Dave 68 TX 70
# Ellen 24 CA 88
# Frank 30 NY 57
df = pd.read_csv('./data/22/sample_pandas_normal.csv', index_col=0)
print(df)
# age state point
# name
# Alice 24 NY 64
# Bob 42 CA 92
# Charlie 18 CA 70
# Dave 68 TX 70
# Ellen 24 CA 88
# Frank 30 NY 57
print(df)
# age state point
# name
# Alice 24 NY 64
# Bob 42 CA 92
# Charlie 18 CA 70
# Dave 68 TX 70
# Ellen 24 CA 88
# Frank 30 NY 57
print(df.loc['Bob'])
# age 42
# state CA
# point 92
# Name: Bob, dtype: object
print(df.at['Bob', 'age'])
