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I have a csv file containing numerical values such as 1524.449677. There are always exactly 6 decimal places.
When I import the csv file (and other columns) via pandas read_csv, the column automatically gets the datatype object. My issue is that the values are shown as 2470.6911370000003 which actually should be 2470.691137. Or the value 2484.30691 is shown as 2484.3069100000002.
This seems to be a datatype issue in some way. I tried to explicitly provide the data type when importing via read_csv by giving the dtype argument as {'columnname': np.float64}. Still the issue did not go away.
How can I get the values imported and shown exactly as they are in the source csv file?
353
CSV file, you cannot keep decimal digits directly. However, you can change the number of decimal places that are displayed for this value. For example, when the data is like 123457.78 in the underlying data, you can change decimal places as 0 to display is like 123457 to export as a . Csv.
Pandas uses a dedicated dec 2 bin converter that compromises accuracy in preference to speed.
Passing float_precision='round_trip' to read_csv fixes this.
Check out this page for more detail on this.
After processing your data, if you want to save it back in a csv file, you can passfloat_format = "%.nf" to the corresponding method.
A full exemple:
import pandas as pd df_in = pd.read_csv(source_file, float_precision='round_trip') df_out = ... # some processing of df_in df_out.to_csv(target_file, float_format="%.3f") # for 3 decimal places If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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