Suppress scientific notation in Pandas *without* altering precision

Question

Is there a way to suppress scientific notation in Panda's outputs without forcing a particular precision across all columns?

So that a data frame:

import pandas as pd

df = pd.DataFrame({"a": [0.01, 0.02, 0.03], "b": [0.0000001, 0.0000002, 0.0000003]})

df.to_csv(
    "df.csv",
    index=False,
)

That initially would be outputted as:

a	b
0.01	1.00E-07
0.02	2.00E-07
0.03	3.00E-07

Instead becomes my desired output:

a	b
0.01	0.0000001
0.02	0.0000002
0.03	0.0000003

Many questions about suppressing scientific notation in Pandas'.to_csv results have already been asked , but all of the answers involve specifying an arbitrary precision.

For instance, setting float_format="%.7f" in df.to_csv forces 7 significant digits for all float columns and numbers (and so does round(7) , of course).

This would lead to the following output, which I don't want:

a	b
0.0100000	0.0000001
0.0200000	0.0000002
0.0300000	0.0000003

(I also tried using np.format_float_positional as suggested here , but had no luck.)

Answer 1

Implement np.format_float_positiona l on a series. If done on df, you will be forced to iterate which can be quite computationally expensive.

Pd.Series

df['b'] =[(lambda x: np.format_float_positional(x))(x) for x in df['b']]

or simply as suggested by @user2357112 supports Monica

df['b'] =[np.format_float_positional(x) for x in df['b']]

Def function Lets try putting this in def function

import numpy as np

def format_float(df):
    
    cols=list(df.columns)
    for col in cols:
        df[col]=[np.format_float_positional(x) for x in df[col]]
        
    return df

format_float(df)

outcome

 a          b
0  0.01  0.0000001
1  0.02  0.0000002
2  0.03  0.0000003