How can I export a DataFrame containing strings and floats to CSV without scientific notation? Transform the numeric column before saving. For example:
import pandas as pd
data = {'category': ['x', 'y', 'z'], 'metric': [1543298765432.789, 9876543210123.456, 1234567890987.123]}
df_mod = pd.DataFrame(data)
df_mod['metric'] = df_mod['metric'].apply(lambda x: format(x, 'f'))
df_mod.to_csv('results.csv', index=False)
print(df_mod)
hey, u can include the option float_format=‘%f’ in the to_csv function. even though it applies to all floats, it often works well with mixed types. thnx
I have experimented with various methods to avoid scientific notation when exporting mixed-type DataFrames, and converting the float values to strings seems to be the most reliable approach. Relying solely on the float_format parameter sometimes doesn’t work well when non-numeric types are involved. Applying a function to transform float values into a standard string format, for example using Python’s format(x, ‘f’), guarantees that the numbers remain in a readable format. This method has consistently provided the control needed over the output format.
I encountered a similar problem while handling sizeable DataFrames, and my solution was to format the numbers post type identification. After filtering out only those columns with floats, I applied a lambda function that converts them to fixed point strings. This method gives you added control on the formatting before exporting to CSV, ensuring consistency in the output. Although minor adjustments are needed for large volumes of data, it reliably maintains precision and avoids unexpected changes in numeric representation.
hey guys, try using .map() to format your floats like this: df[‘col’] = df[‘col’].map(lambda x: ‘{:.6f}’.format(x)). it avoids sci notations and keeps other values intact. hope it helps!