Important initial information: these values are ID's, they are not calculation results, so I really don't have a way to change the way they are saved in the file.

Dataframe example:

But the match_id in the Dataframe appears:

1.19963231
1.2341

And runner_id in the Dataframe appears:

122.2345
101.87

I tried to pass all values as string so it would see the numbers as string and not remove the zeros:

df = pd.read_csv(filial)
df = df.astype(str)

But it didn't help, he kept removing the zero on the right.

I am aware of the existence of float_format but in this case it is necessary to specify the number of decimal places to be used, so I could not use it and as they are ID's I cannot take the risk of a very large value being rounded.

Note: there are hundreds of different columns.

CodePudding user response：

By the time your data is read, the zeros are already removed, so your conversion to str can no longer help.

You need to pass the option directly to read_csv():

df = pd.read_csv(filial, dtype={'runner_id': str})

If you have many columns like this, you can set dtype=str (instead of a dictionary), but then all your columns will be str, so you need to re-parse each of the interesting ones as their correct dtype (e.g. datetime).

More details in the docs ; maybe play with converters param too.