Pands returns the element with the most occurrences in a column (the index corresponding to the maximum value in the series structure after value_counts() counts)

Business scenarios:

1. Read the data and store it in a data box named job_info. 2. Name the column [‘ company ‘, ‘position’, ‘location’, ‘salary’, ‘release date]. 3. Which positions are most in demand? 4. Take out the recruitment information posted on September 3. 5. Find job openings for data analysts based in Shenzhen.

Third, which positions are most in demand? Value_counts () counts the number of occurrences of each element in the post column

The core code

goal = Series.idxmax()

The sample

import pandas as pd

job_info = pd.read_csv('job_info.csv',header=None,names=('the company'.'jobs'.'Place of Work'.'wages'.'Release Date'),encoding = 'gbk')
a = job_info.loc[:,'jobs'].value_counts()   # Count the number of occurrences of each element in the post column
b = a.idxmax()  # Identify the most in-demand positions
data_9_3 = job_info[job_info['Release Date'] = ='09-03']    # Retrieve the job Posting for September 3
data5 = job_info[(job_info['Place of Work'] = ='shenzhen') & (job_info['jobs'] = ='Data analyst')]   Find out recruitment information of data analyst based in Shenzhen
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