Convert all columns from int64 to int32

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We all now the question: Change data type of columns in Pandas where it is really nice explained how to change the data type of a column, but what if I have a dataframe df with the following df.dtypes:

A  object
B   int64
C   int32
D   object
E   int64
F  float32

How could I change this without explicity mention the column names that all int64 types are converted to int32 types? So the desired outcome is:

A  object
B   int32
C   int32
D   object
E   int32
F  float32

You can create dictionary by all columns with int64 dtype by DataFrame.select_dtypes and convert it to int32 by DataFrame.astype, but not sure if not fail if big integers numbers:

df = pd.DataFrame({
        'A':list('abcdef'),
         'B':[4,5,4,5,5,4],
         'C':[7,8,9,4,2,3],
         'D':[1,3,5,7,1,0],
         'E':[5,3,6,9,2,4],
         'F':list('aaabbb')
})


d = dict.fromkeys(df.select_dtypes(np.int64).columns, np.int32)
df = df.astype(d)
print (df.dtypes)
A    object
B     int32
C     int32
D     int32
E     int32
F    object
dtype: object

Change data type of columns in Pandas, convert all DataFrame columns to the int64 dtype df = df.astype(int) # convert Since column 'a' held integer values, it was converted to the Int64 type (which is� // Converted the Int64 value -1 to the Int32 value -1. // Converted the Int64 value 0 to the Int32 value 0. // Converted the Int64 value 121 to the Int32 value 121. // Converted the Int64 value 340 to the Int32 value 340. // The Int64 value 9223372036854775807 is outside the range of the Int32 type.

Use DataFrame.select_dtypes and DataFrame.astype:

# example dataframe
df = pd.DataFrame({'A':list('abc'),
                   'B':[1,2,3],
                   'C':[4,5,6]})

   A  B  C
0  a  1  4
1  b  2  5
2  c  3  6
# as we can see, the integer columns are int64
print(df.dtypes)
A    object
B     int64
C     int64
dtype: object
df = df.astype({col: 'int32' for col in df.select_dtypes('int64').columns})

# int64 columns have been converted to int32
print(df.dtypes)
A    object
B     int32
C     int32
dtype: object

pandas.DataFrame.astype — pandas 1.1.0 documentation, Cast a pandas object to a specified dtype dtype . Parameters DataFrame(data= d) >>> df.dtypes col1 int64 col2 int64 dtype: object. Cast all columns to int32:. // Converted the Int32 value -12 to the Int64 value -12. // Converted the Int32 value 163 to the Int64 value 163. // Converted the Int32 value 935 to the Int64 value 935. // Converted the Char value x to the Int64 value 120. // No conversion to an Int64 exists for the DateTime value 5/12/2009 12:00:00 AM.

You can also use a for loop to iterate through the df's columns and check their datatype.

df = pd.DataFrame({'A': ['a','b','c'],
                   'B': [1,2,3],
                   'C': [4.0,5.0,6.0]})
print(df.dtypes)

A     object
B      int64
C    float64
dtype: object

#if it's int64 set it as int32:
for column in df.columns:
    if df[column].dtype == 'int64':
        df[column] = df[column].astype('int32')
print(df.dtypes)

A     object
B      int32
C    float64
dtype: object

pandas.DataFrame.astype — pandas 0.25.3 documentation, 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. Cast all columns to int32: df.astype({'col1': 'int32'}).dtypes col1 int32 col2 int64 dtype: object. Name object Age int64 City object Marks int64 dtype: object Now to convert the data type of 2 columns i.e. ‘Age’ & ‘Marks’ from int64 to float64 & string respectively, we can pass a dictionary to the Dataframe.astype().

How to convert a Pandas DataFrame column from object to int in , Series.astype() twice to convert a DataFrame column from object to int The int64 data type is pandas 64-bit version of int used in their DataFrames. To create � I am modifying some legacy code that is using datatable.compute to convert the string columln to int. my question is why I am getting this exception with this line . var sum = dt.Compute("Sum(Convert(ID, 'System.Int32'))", "");

Changing data type, Method 1: Change datatype after reading the csv country object beer_servings float64 spirit_servings int64 To find out whether a column's row contains a certain string by return How to convert string column elements to int elements column in data frames?? 2020 Ritchie Ng. All rights reserved. By using the options convert_string, convert_integer, and convert_boolean, it is possible to turn off individual conversions to StringDtype, the integer extension types or BooleanDtype, respectively. For object-dtyped columns, if infer_objects is True , use the inference rules as during normal Series/DataFrame construction.

dtype data type, or dict of column name -> data type. Use a numpy.dtype or Python type to cast 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