Pandas DataFrame-通过比较创建新列

Question

I'm trying to create a columns called 'city_code' with values from the 'code' column. But in order to do this I need to compare if 'ds_city' and 'city' values are equal.

Here is a table sample:

https://i.imgur.com/093GJF1.png

I've tried this:

def find_code(data):
    if data['ds_city'] == data['city'] :
        return data['code']
    else:
        return 'UNKNOWN'

df['code_city'] = df.apply(find_code, axis=1)

But since there are duplicates in the 'ds_city' columns that's the result:

https://i.imgur.com/geHyVUA.png

Here is a image of the expected result:

https://i.imgur.com/HqxMJ5z.png

How can I work around this?

Answer 1

You can use pandas merge:

df = pd.merge(df, df[['code', 'city']], how='left', 
              left_on='ds_city', right_on='city', 
              suffixes=('', '_right')).drop(columns='city_right')

# output:
#   code    city        ds_city     code_right
# 0 1500107 ABAETETUBA  ABAETETUBA  1500107
# 1 2900207 ABARE       ABAETETUBA  1500107
# 2 2100055 ACAILANDIA  ABAETETUBA  1500107
# 3 2300309 ACOPIARA    ABAETETUBA  1500107
# 4 5200134 ACREUNA     ABARE       2900207

Here's pandas.merge's documentation . It takes the input dataframe and left joins itself's code and city columns when ds_city equals city .

The above code will fill code_right when city is not found with nan . You can further do the following to fill it with 'UNKNOWN':

df['code_right'] = df['code_right'].fillna('UNKNOWN')

Answer 2

This is more like np.where

import numpy as np 

df['code_city'] = np.where(data['ds_city'] == data['city'],data['code'],'UNKNOWN')

Answer 3

You could try this out:

# Begin with a column of only 'UNKNOWN' values.
data['code_city'] = "UNKNOWN"
# Iterate through the cities in the ds_city column.
for i, lookup_city in enumerate(data['ds_city']):
  # Note the row which contains the corresponding city name in the city column.
  row = data['city'].tolist().index(lookup_city)
  # Reassign the current row's code_city column to that code from the row we found in the last step.
  data['code_city'][i] = data['code'][row]