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Adjusting number of rows that are printed Appending DataFrame to an existing CSV file Checking differences between two indexes Checking if a DataFrame is empty Checking if a variable is a DataFrame Checking if index is sorted Checking if value exists in Index Checking memory usage of DataFrame Checking whether a Pandas object is a view or a copy Concatenating a list of DataFrames Converting a DataFrame to a list Converting a DataFrame to a Series Converting DataFrame to a list of dictionaries Converting DataFrame to list of tuples Counting the number of negative values Creating a DataFrame using cartesian product of two DataFrames Displaying DataFrames side by side Displaying full non-truncated DataFrame values Drawing frequency histogram of DataFrame column Exporting Pandas DataFrame to PostgreSQL table Highlighting a particular cell of a DataFrame Highlighting DataFrame cell based on value How to solve "ValueError: If using all scalar values, you must pass an index"Importing BigQuery table as Pandas DataFrame Plotting two columns of DataFrame Printing DataFrame on a single line Printing DataFrame without index Printing DataFrames in tabular format Randomly splitting DataFrame into multiple DataFrames of equal size Reducing DataFrame memory size Saving a DataFrame as a CSV file Saving DataFrame as Excel file Saving DataFrame as feather file Setting all values to zero Showing all dtypes without truncation Splitting DataFrame into multiple DataFrames based on value Splitting DataFrame into smaller equal-sized DataFrames Writing DataFrame to SQLite

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Appending DataFrame to an existing CSV file in Pandas

schedule Aug 11, 2023

Last updated

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Python●Pandas

Example

Consider the following CSV file called "my_data.txt":

Now, suppose we wanted to append the following DataFrame to "my_data.txt":


        
        
            
                
                
                    df = pd.DataFrame({"A":[12,13],"B":[14,15]})
df
                
            
               A  B
0  2  4
1  3  5

Call the to_csv(~) method with mode="a" for append mode, and header=False since we already have headers in the file:


        
        
            
                
                
                    df.to_csv("my_data.txt", mode="a", header=False)

Our "my_data.txt" file should now look like this:

Pandas DataFrame | to_csv method

Converts the source DataFrame into comma-separated value format.

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Published by Isshin Inada

Edited by 0 others

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Appending DataFrame to an existing CSV file in Pandas

Example

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