Python 如何更改 Pandas 数据框索引值？
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How to change Pandas dataframe index value?
提问by bigbug
I have a df:
我有一个df：
>>> df
sales cash
STK_ID RPT_Date
000568 20120930 80.093 57.488
000596 20120930 32.585 26.177
000799 20120930 14.784 8.157
And want to change first row's index value from ('000568','20120930')to ('000999','20121231'). Final result will be:
并希望将第一行的索引值从 更改('000568','20120930')为('000999','20121231')。最终结果将是：
>>> df
sales cash
STK_ID RPT_Date
000999 20121231 80.093 57.488
000596 20120930 32.585 26.177
000799 20120930 14.784 8.157
How to achieve this?
如何实现这一目标？
采纳答案by unutbu
With this setup:
使用此设置：
import pandas as pd
import io
text = '''\
STK_ID RPT_Date sales cash
000568 20120930 80.093 57.488
000596 20120930 32.585 26.177
000799 20120930 14.784 8.157
'''
df = pd.read_csv(io.BytesIO(text), delimiter = ' ',
converters = {0:str})
df.set_index(['STK_ID','RPT_Date'], inplace = True)
The index, df.indexcan be reassigned to a new MultiIndexlike this:
索引，df.index可以MultiIndex像这样重新分配给一个新的：
index = df.index
names = index.names
index = [('000999','20121231')] + df.index.tolist()[1:]
df.index = pd.MultiIndex.from_tuples(index, names = names)
print(df)
# sales cash
# STK_ID RPT_Date
# 000999 20121231 80.093 57.488
# 000596 20120930 32.585 26.177
# 000799 20120930 14.784 8.157
Or, the index could be made into columns, the values in the columns could be then reassigned, and then the columns returned to indices:
或者，可以将索引制成列，然后可以重新分配列中的值，然后将列返回到索引：
df.reset_index(inplace = True)
df.ix[0, ['STK_ID', 'RPT_Date']] = ('000999','20121231')
df = df.set_index(['STK_ID','RPT_Date'])
print(df)
# sales cash
# STK_ID RPT_Date
# 000999 20121231 80.093 57.488
# 000596 20120930 32.585 26.177
# 000799 20120930 14.784 8.157
Benchmarking with IPython %timeitsuggests reassigning the index (the first method, above) is significantly faster than resetting the index, modifying column values, and then setting the index again (the second method, above):
使用 IPython 进行基准测试%timeit表明，重新分配索引（上面的第一种方法）比重置索引、修改列值，然后再次设置索引（上面的第二种方法）要快得多：
In [2]: %timeit reassign_index(df)
10000 loops, best of 3: 158 us per loop
In [3]: %timeit reassign_columns(df)
1000 loops, best of 3: 843 us per loop
