How to add pandas data to an existing csv file?

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I want to know if it is possible to use the pandas to_csv() function to add a dataframe to an existing csv file. The csv file has the same structure as the loaded data.

You can specify a python write mode in the pandas to_csv function. For append it is ‘a’.

In your case:

df.to_csv('my_csv.csv', mode="a", header=False)

The default mode is ‘w’.

If the file initially might be missing, you can make sure the header is printed at the first write using this variation:

df.to_csv(output_path, mode="a", header=not os.path.exists(output_path))

You can append to a csv by opening the file in append mode:

with open('my_csv.csv', 'a') as f:
    df.to_csv(f, header=False)

If this was your csv, foo.csv:


If you read that and then append, for example, df + 6:

In [1]: df = pd.read_csv('foo.csv', index_col=0)

In [2]: df
   A  B  C
0  1  2  3
1  4  5  6

In [3]: df + 6
    A   B   C
0   7   8   9
1  10  11  12

In [4]: with open('foo.csv', 'a') as f:
             (df + 6).to_csv(f, header=False)

foo.csv becomes:


with open(filename, 'a') as f:
    df.to_csv(f, header=f.tell()==0)
  • Create file unless exists, otherwise append
  • Add header if file is being created, otherwise skip it

A little helper function I use with some header checking safeguards to handle it all:

def appendDFToCSV_void(df, csvFilePath, sep=","):
    import os
    if not os.path.isfile(csvFilePath):
        df.to_csv(csvFilePath, mode="a", index=False, sep=sep)
    elif len(df.columns) != len(pd.read_csv(csvFilePath, nrows=1, sep=sep).columns):
        raise Exception("Columns do not match!! Dataframe has " + str(len(df.columns)) + " columns. CSV file has " + str(len(pd.read_csv(csvFilePath, nrows=1, sep=sep).columns)) + " columns.")
    elif not (df.columns == pd.read_csv(csvFilePath, nrows=1, sep=sep).columns).all():
        raise Exception("Columns and column order of dataframe and csv file do not match!!")
        df.to_csv(csvFilePath, mode="a", index=False, sep=sep, header=False)

Initially starting with a pyspark dataframes – I got type conversion errors (when converting to pandas df’s and then appending to csv) given the schema/column types in my pyspark dataframes

Solved the problem by forcing all columns in each df to be of type string and then appending this to csv as follows:

with open('testAppend.csv', 'a') as f:
    df2.toPandas().astype(str).to_csv(f, header=False)

A bit late to the party but you can also use a context manager, if you’re opening and closing your file multiple times, or logging data, statistics, etc.

from contextlib import contextmanager
import pandas as pd
def open_file(path, mode):
     yield file_to

with open_file('yourcsv.csv','r') as infile:

This is how I did it in 2021

Let us say I have a csv sales.csv which has the following data in it:


Order Name,Price,Qty

and to add more rows I can load them in a data frame and append it to the csv like this:

import pandas

data = [
    ['matchstick', '60', '11'],
    ['cookies', '10', '120']
dataframe = pandas.DataFrame(data)
dataframe.to_csv("sales.csv", index=False, mode="a", header=False)

and the output will be:

Order Name,Price,Qty

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