Hello handsome brother
The operating system for the project will be Windows, as most personal and professional computers run on this system. However, Python is a cross-platform language, so the solution can be implemented on any operating system as long as the necessary libraries are installed.
How many files you have and what is the size of the files.
There are 500 CSV files in total, with each file being approximately 1-2 MB in size.
Here is the solution for pivot tables on multiple csv files using python:
Step 1: Import necessary libraries
The first step is to import the necessary libraries for the solution, which include pandas, glob, and os. These libraries will help us read the CSV files, perform data manipulation, and traverse through multiple files.
Step 2: Create a list of file paths
Using the glob library, we can create a list of file paths of all 500 CSV files in a directory. This can be done by specifying the directory where the files are located and using the *.csv pattern to select all CSV files.
Step 3: Create an empty dataframe
Next, we will create an empty dataframe that will store the results of our pivot tables. We can do this by using the pandas library and the DataFrame function.
Step 4: Loop through each file and extract key variables
Using a for loop, we can iterate through each file in the list of file paths and extract two key variables or data points from each file. This can be done by using the pandas library and the read_csv function to read the CSV files and selecting the desired variables using column names.
Step 5: Populate the empty dataframe
As we extract the key variables from each file, we can populate our empty dataframe with the values. This can be done by using the pandas library and the append function to add the values to the dataframe.
Step 6: Create pivot table
Once all the values have been added to the dataframe, we can use the pandas library and the pivot_table function to create a pivot table. On this pivot table, we can specify the two key variables as the index and columns to compare and the desired calculation for the values.
Step 7: Save the pivot table as a new CSV file
Lastly, we can save the pivot table as a new CSV file using the to_csv function from the pandas library. This new CSV file will contain the results of our pivot tables on all 500 CSV files.
By automating this process, we will be able to save time and effort as opposed to manually creating pivot tables for each individual file. Moreover, this solution can also be modified to work with files in different formats or with more than just two key variables. It provides a fast and efficient way to perform data analysis and allows for easy manipulation and customization of the pivot tables.
Best regards,
Giáp Văn Hưng
