Python 在 DataFrame 索引中查找标签位置
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Finding label location in a DataFrame Index
提问by nitin
I have a pandas dataframe:
我有一个熊猫数据框：
import pandas as pnd
d = pnd.Timestamp('2013-01-01 16:00')
dates = pnd.bdate_range(start=d, end = d+pnd.DateOffset(days=10), normalize = False)
df = pnd.DataFrame(index=dates, columns=['a'])
df['a'] = 6
print(df)
a
2013-01-01 16:00:00 6
2013-01-02 16:00:00 6
2013-01-03 16:00:00 6
2013-01-04 16:00:00 6
2013-01-07 16:00:00 6
2013-01-08 16:00:00 6
2013-01-09 16:00:00 6
2013-01-10 16:00:00 6
2013-01-11 16:00:00 6
I am interested in find the label location of one of the labels, say,
我有兴趣找到其中一个标签的标签位置，例如，
ds = pnd.Timestamp('2013-01-02 16:00')
Looking at the index values, I know that is integer location of this label 1. How can get pandas to tell what the integer value of this label is?
查看索引值，我知道这是这个标签的整数位置 1. 如何让熊猫知道这个标签的整数值是什么？
采纳答案by Andy Hayden
You're looking for the index method get_loc:
您正在寻找 index 方法get_loc：
In [11]: df.index.get_loc(ds)
Out[11]: 1
回答by Eric Leschinski
Get dataframe integer index given a date key:
获取给定日期键的数据帧整数索引：
>>> import pandas as pd
>>> df = pd.DataFrame(
index=pd.date_range(pd.datetime(2008,1,1), pd.datetime(2008,1,5)),
columns=("foo", "bar"))
>>> df["foo"] = [10,20,40,15,10]
>>> df["bar"] = [100,200,40,-50,-38]
>>> df
foo bar
2008-01-01 10 100
2008-01-02 20 200
2008-01-03 40 40
2008-01-04 15 -50
2008-01-05 10 -38
>>> df.index.get_loc(df["bar"].argmax())
1
>>> df.index.get_loc(df["foo"].argmax())
2
In column bar, the index of the maximum value is 1
在列栏中，最大值的索引为 1
In column foo, the index of the maximum value is 2
在 foo 列中，最大值的索引为 2
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.get_loc.html
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.get_loc.html
回答by Emad Saeidi
get_loccan be used for rowsand columnsaccording to:
get_loc可用于行和列，根据：
import pandas as pnd
d = pnd.Timestamp('2013-01-01 16:00')
dates = pnd.bdate_range(start=d, end = d+pnd.DateOffset(days=10), normalize = False)
df = pnd.DataFrame(index=dates)
df['a'] = 5
df['b'] = 6
print(df.head())
a b
2013-01-01 16:00:00 5 6
2013-01-02 16:00:00 5 6
2013-01-03 16:00:00 5 6
2013-01-04 16:00:00 5 6
2013-01-07 16:00:00 5 6
#for rows
print(df.index.get_loc('2013-01-01 16:00:00'))
0
#for columns
print(df.columns.get_loc('b'))
1
