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.
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7I think method suggested by @tlingf is better only because he is using build-in functionality of pandas library. He suggests define mode as "a" . "A" stands for APPEND 'df.to_csv('my_csv.csv', mode='a', header=False)'Ayrat– Ayrat2014-10-20 13:14:07 +00:00Commented Oct 20, 2014 at 13:14
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3The answer from @KCzar considers both the cases when the CSV file is not there (i.e. add the column header) and when the CSV is already there (so add just the data rows without headers). In any case it uses the "append" mode and a custom separator, along with checks on the number of columns.TPPZ– TPPZ2019-04-17 08:46:26 +00:00Commented Apr 17, 2019 at 8:46
7 Answers
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:
output_path='my_csv.csv'
df.to_csv(output_path, mode='a', header=not os.path.exists(output_path))
6 Comments
df.to_csv(output_path, mode='a', header=not os.path.exists(output_path))index=False will tell df.to_csv not to write the row index to the first column. Depending on the application, this might make sense to avoid a meaningless index column.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:
,A,B,C
0,1,2,3
1,4,5,6
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
Out[2]:
A B C
0 1 2 3
1 4 5 6
In [3]: df + 6
Out[3]:
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:
,A,B,C
0,1,2,3
1,4,5,6
0,7,8,9
1,10,11,12
2 Comments
with open('my_csv.csv', 'a') as f:??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
3 Comments
mode='a' as a parameter to to_csv (ie df.to_csv(f, mode='a', header=f.tell()==0)header=(f.tell()==0) -- and also write : with open(filename, 'a', newline='') as f: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!!")
else:
df.to_csv(csvFilePath, mode='a', index=False, sep=sep, header=False)
2 Comments
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)
Comments
This is how I did it in 2021
Let us say I have a csv sales.csv which has the following data in it:
sales.csv:
Order Name,Price,Qty
oil,200,2
butter,180,10
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
oil,200,2
butter,180,10
matchstick,60,11
cookies,10,120
2 Comments
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
@contextmanager
def open_file(path, mode):
file_to=open(path,mode)
yield file_to
file_to.close()
##later
saved_df=pd.DataFrame(data)
with open_file('yourcsv.csv','r') as infile:
saved_df.to_csv('yourcsv.csv',mode='a',header=False)`