Write comments in CSV file with pandas
Question:
I would like to write some comments in my CSV file created with pandas. I haven’t found any option for this in DataFrame.to_csv (even though read_csv can skip comments) neither in the standard csv module. I can open the file, write the comments (line starting with #) and then pass it to to_csv. Does any body have a better option?
Answers:
df.to_csv accepts a file object. So you can open a file in a mode, write you comments and pass it to the dataframe to_csv function.
For example:
In [36]: df = pd.DataFrame({'a':[1,2,3], 'b':[1,2,3]})
In [37]: f = open('foo', 'a')
In [38]: f.write('# My awesome commentn')
In [39]: f.write('# Here is another onen')
In [40]: df.to_csv(f)
In [41]: f.close()
In [42]: more foo
# My awesome comment
# Here is another one
,a,b
0,1,1
1,2,2
2,3,3
An alternative approach @Vor’s solution is to first write the comment to a file, and then use mode='a' with to_csv() to add the content of the data frame to the same file. According to my benchmarks (below), this takes about as long as opening the file in append mode, adding the comment and then passing the file handler to pandas (as per @Vor’s answer). The similar timings make sense considering that this is what pandas in doing internally (DataFrame.to_csv() calls CSVFormatter.save(), which uses _get_handles() to read in the file via open().
On a separate note, it is convenient work with file IO via with statement which ensures that opened files close when you’re done with them and leave the with statement. See examples in the benchmarks below.
Read in test data
import pandas as pd
# Read in the iris data frame from the seaborn GitHub location
iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
# Create a bigger data frame
while iris.shape[0] < 100000:
iris = iris.append(iris)
# `iris.shape` is now (153600, 5)
1. Append with the same file handler
%%timeit -n 5 -r 5
# Open a file in append mode to add the comment
# Then pass the file handle to pandas
with open('test1.csv', 'a') as f:
f.write('# This is my commentn')
iris.to_csv(f)
972 ms ± 31.9 ms per loop (mean ± std. dev. of 5 runs, 5 loops each)
2. Reopen the file with to_csv(mode='a')
%%timeit -n 5 -r 5
# Open a file in write mode to add the comment
# Then close the file and reopen it with pandas in append mode
with open('test2.csv', 'w') as f:
f.write('# This is my commentn')
iris.to_csv('test2.csv', mode='a')
949 ms ± 19.3 ms per loop (mean ± std. dev. of 5 runs, 5 loops each)
I would like to write some comments in my CSV file created with pandas. I haven’t found any option for this in DataFrame.to_csv (even though read_csv can skip comments) neither in the standard csv module. I can open the file, write the comments (line starting with #) and then pass it to to_csv. Does any body have a better option?
df.to_csv accepts a file object. So you can open a file in a mode, write you comments and pass it to the dataframe to_csv function.
For example:
In [36]: df = pd.DataFrame({'a':[1,2,3], 'b':[1,2,3]})
In [37]: f = open('foo', 'a')
In [38]: f.write('# My awesome commentn')
In [39]: f.write('# Here is another onen')
In [40]: df.to_csv(f)
In [41]: f.close()
In [42]: more foo
# My awesome comment
# Here is another one
,a,b
0,1,1
1,2,2
2,3,3
An alternative approach @Vor’s solution is to first write the comment to a file, and then use mode='a' with to_csv() to add the content of the data frame to the same file. According to my benchmarks (below), this takes about as long as opening the file in append mode, adding the comment and then passing the file handler to pandas (as per @Vor’s answer). The similar timings make sense considering that this is what pandas in doing internally (DataFrame.to_csv() calls CSVFormatter.save(), which uses _get_handles() to read in the file via open().
On a separate note, it is convenient work with file IO via with statement which ensures that opened files close when you’re done with them and leave the with statement. See examples in the benchmarks below.
Read in test data
import pandas as pd
# Read in the iris data frame from the seaborn GitHub location
iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
# Create a bigger data frame
while iris.shape[0] < 100000:
iris = iris.append(iris)
# `iris.shape` is now (153600, 5)
1. Append with the same file handler
%%timeit -n 5 -r 5
# Open a file in append mode to add the comment
# Then pass the file handle to pandas
with open('test1.csv', 'a') as f:
f.write('# This is my commentn')
iris.to_csv(f)
972 ms ± 31.9 ms per loop (mean ± std. dev. of 5 runs, 5 loops each)
2. Reopen the file with to_csv(mode='a')
%%timeit -n 5 -r 5
# Open a file in write mode to add the comment
# Then close the file and reopen it with pandas in append mode
with open('test2.csv', 'w') as f:
f.write('# This is my commentn')
iris.to_csv('test2.csv', mode='a')
949 ms ± 19.3 ms per loop (mean ± std. dev. of 5 runs, 5 loops each)