How To Convert DataType Of The Columns In DataFrame?

(1) Syntax:

DataFrame.astype(dtype, copy=True, errors='raise')

Description:

Parameters:

dtype: Use a numpy.dtype or Python type to cast the entire pandas object to the same type. Alternatively, use {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.
copy: bool, default True – Return a copy when copy=True (be very careful setting copy=False as changes to values then may propagate to other pandas objects).
errors{‘raise’, ‘ignore’}, default ‘raise’ –
Control raising of exceptions on invalid data for provided dtype.
- raise : allow exceptions to be raised
- ignore : suppress exceptions. On error return the original object.

Returns:

d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d)
df.dtypes

col1    int64
col2    int64
dtype: object

df.astype('int32').dtypes

col1    int32
col2    int32
dtype: object

d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d)
df.dtypes

col1    int64
col2    int64
dtype: object

df.astype({'col1': 'int32'}).dtypes

col1    int32
col2    int64
dtype: object

d = {'col1': [1, 2], 'col2': [3, 4]}
df1 = pd.DataFrame(data=d)
df1.dtypes

col1    int64
col2    int64
dtype: object

df2 = df1.astype('int64',copy=False)
df2['col1'][0] = 99
df1

Note:

Here you can see that when you set copy=False, if you change the second DataFrame the First one is also changed.
That means it has not been copied.