Data extraction is a common requirement for analysts in their daily work. For example, the loan amount of a user, the total interest income of a month or quarter, the loan amount and number of transactions in a specific period of time, and the number of loans larger than 5000 yuan. This article describes how to extract data using python based on specific dimensions or conditions to meet data extraction requirements. Data extraction is a common requirement for analysts in their daily work. For example, the loan amount of a user, the total interest income of a month or quarter, the loan amount and number of transactions in a specific period of time, and the number of loans larger than 5000 yuan. This article describes how to extract data using python based on specific dimensions or conditions to meet data extraction requirements.
Preparations
The first step is preparation. import the required database and read and create a data table named loandata.
Import numpy as npimport pandas as pdloandata1_pd.dataframe(pd.read_excel('loan_data.xlsx '))
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