How do we remove zeros after the comma in certain columns?

[Amritpal]

My code is:

df_CG1=pd.read_csv('payments_CG1.csv' ,sep=';')
pd.set_option('display.max_rows', None)
df_CG1=df_CG1.dropna(how= 'all')
df_CG1

Please indicate as a column: user_id♪ revenueand payment_service_id I'd like you to put "objectives" to look beautiful.

[rosemadder]

Maybe you get the type of data. float64'Cause in some lines, you're dating. NaN values and whole types in Numpy do not understand NaN and therefore Pandas automatically converts such columns into data type float64♪

In modern versions, Pandas introduced a new type of data "Int64" (with a large letter) which understands the values NaN♪

Example:

In [13]: df = pd.DataFrame([[1, 2, 3], [np.nan, 3, np.nan]], columns=list("abc"))
In [14]: df

Out[14]:

a  b    c

0  1.0  2  3.0

1  NaN  3  NaN
In [17]: cols = df.columns[df.dtypes.eq("float64")]
In [18]: df[cols] = df[cols].astype(int)
ValueError                                Traceback (most recent call last)

...

ValueError: Cannot convert non-finite values (NA or inf) to integer

...

Decision:

In [19]: df[cols] = df[cols].astype("Int64")

In [20]: df

Out[20]:

a  b     c

0     1  2     3

1  <NA>  3  <NA>
In [21]: df.dtypes

Out[21]:

a    Int64

b    int64

c    Int64

dtype: object