这个问题和我上一个问题差不多,但是我有一个DF。
Index Batch Name List Name
0 1 Jon Adam
1
2 2 Adam Sam
3 Chris
4 3 Voges Jon
5
6 4 Jon Voges
我想在列表名称中搜索每个值的批号,即Adam、Sam、Chris、Jon和Voges。我想要另一个DF,如下所示
Index Batch Name List Name BatchNames
0 1 Jon Adam Adam(2)
1
2 2 Adam Sam Sam(2)
3 Chris Chris(2)
4 3 Voges Jon Jon(1,4)
5
6 4 Jon Voges Voges(3)
我想选择每个列表名称并在名称中搜索对应的批号,即1 and 4
中存在Jon
,依此类推。但如果Listname中的某个名称在Name中不存在,则应选择与其相近的对应批号,例如Name中不存在Sam
,但它与Batch 2
接近,Chris
也是如此。基本上,批次之间存在的任何内容都属于最低批次编号。我该如何为此编写一个自定义函数?
发布于 2018-06-25 05:31:37
我会这样做:
import pandas as pd
import numpy as np
def custom_function(df):
# Forward fill the Batch number
df_Batch = df.Batch.copy()
df.Batch.ffill(inplace=True)
df.Batch = df.Batch.astype(int)
# Make a new dataframe where we first get batches for the name column
# and append batches for the list name column, there we be duplicates so we keep the first entry
a = df.groupby('Name').Batch.apply(tuple).append(df.groupby('List Name').Batch.apply(tuple)).reset_index().groupby('index').first()
# Create a series which concatenates the Batch number and List Name
b = pd.Series(a.index.astype(str) + a.Batch.astype(str), index=a.index).replace(',','', regex=True).replace(' ',',',regex=True)
# undo the forward fill (replace with original columns)
df.Batch = df_Batch
# join the series we just made to the dataframe
return df.merge(b.to_frame().rename_axis('List Name'), how='left', on='List Name', suffixes=['', 'Names']).fillna('')
df = pd.DataFrame({'Batch':[1,np.nan,2,np.nan,3,np.nan,4], 'Name':['Jon',np.nan, 'Adam',np.nan, 'Voges',np.nan, 'Jon'], 'List Name':['Adam', np.nan, 'Sam', 'Chris', 'Jon', np.nan, 'Voges']})
# Out[122]:
# Batch Name List Name
# 0 1.0 Jon Adam
# 1 NaN NaN NaN
# 2 2.0 Adam Sam
# 3 NaN NaN Chris
# 4 3.0 Voges Jon
# 5 NaN NaN NaN
# 6 4.0 Jon Voges
custom_function(df)
# Out[131]:
# Batch Name List Name BatchNames
# 0 1 Jon Adam Adam(2)
# 1
# 2 2 Adam Sam Sam(2)
# 3 Chris Chris(2)
# 4 3 Voges Jon Jon(1,4)
# 5
# 6 4 Jon Voges Voges(3)
https://stackoverflow.com/questions/51012626
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