"Data Science 100 Knock (Structured Data Processing)" Python-007 Explanation

Youtube Video commentary is also available.

problem

P-007: Specify the columns in the order of sales date (sales_ymd), customer ID (customer_id), product code (product_cd), sales amount (amount) from the receipt detail data frame (df_receipt), and select the data that meets the following conditions Extract. --Customer ID (customer_id) is "CS018205000001" --Sales amount (amount) is 1,000 or more and 2,000 or less

answer

code


df_receipt[['sales_ymd', 'customer_id', 'product_cd', 'amount']] \
.query('customer_id == "CS018205000001" & 1000 <= amount <= 2000 ')

output


       sales_ymd  customer_id     product_cd  amount
72254  20180911   CS018205000001  P071401005  1100

Commentary

**-In Pandas DataFrame / Series, while specifying columns, it is a method to check the rows that meet the conditions among the specified rows. -Use this when you want to narrow down the column information, specify the row, and check the information that meets the conditions. ยท' [['','','']]] .guery (' == " In "& 1000 <= <= 2000')', among the specified columns (column name A, column name B, column name C), the row corresponding to the row information A specified by column name A. And, the row with column name B of 1000 or more and 2000 or less is displayed. ** **

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