![]() ![]() You can customize the code according to your requirements, such as loading data from a database or a CSV file and transforming it into a DataFrame, or specifying additional options such as the delimiter, encoding, and more. Pandas automatically writes the header row based on the DataFrame column names and writes the data rows with the corresponding values. ![]() We provide the filename as the first parameter and set the index parameter to False to exclude the index column from the output. Next, we write the DataFrame to a CSV file using the to_csv() function. Each key in the dictionary represents a column name, and the corresponding value represents the column data. In the above code, we create a DataFrame with the data using a Python dictionary. # Create a DataFrame with the data data = df = pd.DataFrame(data) # Write the DataFrame to a CSV file df.to_csv( ' output_file.csv ', index = False ) Python You can filter CSV data using Python by reading the CSV file into a pandas DataFrame and then using the various methods available in pandas to filter the data. How to filter CSV data using Pythonįilter the data based on your criteria. The index=False parameter specifies that we do not want to write the row index to the CSV file. ![]() The df] statement selects the ‘Name’ and ‘Age’ columns by name, while the df.iloc] statement selects the first and third columns (i.e., ‘Name’ and ‘Salary’) by index.Īfter selecting the desired columns, we export the resulting DataFrame to a new CSV file named ‘selected_data.csv’ using the to_csv() function. We then select specific columns from the DataFrame df using their names or indices. In this example, we first read a CSV file named ‘data.csv’ into a DataFrame df using the read_csv() function. Import pandas as pd # Read the CSV file into a DataFrame df = pd.read_csv( ' data.csv ' ) # Select specific columns by name selected_cols = df] # Select specific columns by index selected_cols = df.iloc] # Export the selected columns to a new CSV file selected_cols.to_csv( ' selected_data.csv ', index = False ) Python ![]()
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