CSV file handling with Pandas for text comparison-CodePudding

I have this csv file called input.csv

KEY;Rate;BYld;DataAsOfDate
CH04;0.719;0.674;2020-01-29
CH03;1.5;0.148;2020-01-29

then I execute the following code:

import pandas as pd
input_df = pd.read_csv('input.csv', sep=";")
input_df.to_csv('output.csv', sep=";")

and get the following output.csv file

KEY;Rate;BYld;DataAsOfDate
CH04;0.7190000000000001;0.674;2020-01-29
CH03;1.5;0.14800000000000002;2020-01-29

I was hoping for and expecting an output like this: (to be able to use a tool like winmerge.org to detect real differences on each row) (my real code truly modifies the dataframe - this stack overflow example is for demonstration only)

KEY;Rate;BYld;DataAsOfDate
CH04;0.719;0.674;2020-01-29
CH03;1.5;0.148;2020-01-29

What is the idiomatic way with to achieve such an unmodified output with Pandas?

CodePudding user response：

Python does not use traditional rounding to so as to prevent problems with bankers rounding. However, if being close is not a problem you could use the round function and replace the "2" with whichever number you would like to round to

d = [['CH04',0.719,0.674,'2020-01-29']]
df = pd.DataFrame(d, columns = (['KEY', 'Rate', 'BYld', 'DataAsOfDate']))
df['Rate'] = df['Rate'].apply(lambda x : round(x, 2))
df

CodePudding user response：

Using @Prokos idea I changed the code like this:

import pandas as pd
input_df = pd.read_csv('input.csv', dtype='str',sep=";")
input_df.to_csv('str_output.csv', sep=";", index=False)

and that meets the requirement - all columns come out unchanged.