The specific use of pandas DataFrame convert_dtypes

Pandas2.2 DataFrame

Conversion

.convert_dtypes

.convert_dtypesis a method to convert data types in DataFrame to more appropriate types. This method can help automatically infer and convert data types, making data processing more efficient and accurate.

Method signature

DataFrame.convert_dtypes(infer_objects=True, convert_string=True, convert_integer=True, convert_boolean=True, convert_floating=True, dtype_backend='numpy_nullable')

Parameter description

• infer_objects: Boolean value, default isTrue, indicating whether to try toobjectConvert a column of type to a more specific type (e.g.int64orfloat64）。
• convert_string: Boolean value, default isTrue, indicating whether it willobjectConvert columns of type tostringtype.
• convert_integer: Boolean value, default isTrue, indicating whether it willobjectConvert columns of type tointegertype.
• convert_boolean: Boolean value, default isTrue, indicating whether it willobjectConvert columns of type tobooleantype.
• convert_floating: Boolean value, default isTrue, indicating whether it willobjectConvert columns of type tofloatingtype.
• dtype_backend: string, default is'numpy_nullable', indicates the data type backend used. Could be'numpy_nullable'or'pyarrow'。

Return value

• Returns a new DataFrame where the data type has been converted.

Example

Suppose there is a DataFrame as follows:

import pandas as pd

data = {
    'A': ['1', '2', '3'],
    'B': ['1.1', '2.2', '3.3'],
    'C': ['True', 'False', 'True'],
    'D': ['x', 'y', 'z']
}

df = (data)
print("Original DataFrame:")
print(df)
print("\nData Type:")
print()

Output:

Original DataFrame:
   A    B      C  D
0  1  1.1   True  x
1  2  2.2  False  y
2  3  3.3   True  z

Data Type:
A    object
B    object
C    object
D    object
dtype: object

Example 1: Convert data types using default parameters

df_converted = df.convert_dtypes()
print("Converted DataFrame:")
print(df_converted)
print("\nData Type:")
print(df_converted.dtypes)

result:

The converted DataFrame:
   A    B      C  D
0  1  1.1   True  x
1  2  2.2  False  y
2  3  3.3   True  z

Data Type:
A    Int64
B  Float64
C    boolean
D    string
dtype: object

Example 2: Disable convert_string

df_converted_no_string = df.convert_dtypes(convert_string=False)
print("DataFrame after convert_string is disabled:")
print(df_converted_no_string)
print("\nData Type:")
print(df_converted_no_string.dtypes)

result:

Disable DataFrame after convert_string:
   A    B      C  D
0  1  1.1   True  x
1  2  2.2  False  y
2  3  3.3   True  z

Data Type:
A    Int64
B  Float64
C    boolean
D    object
dtype: object

Example 3: Disable convert_integer

df_converted_no_integer = df.convert_dtypes(convert_integer=False)
print("DataFrame after convert_integer is disabled:")
print(df_converted_no_integer)
print("\nData Type:")
print(df_converted_no_integer.dtypes)

result:

Disable DataFrame after convert_integer:
   A    B      C  D
0  1  1.1   True  x
1  2  2.2  False  y
2  3  3.3   True  z

Data Type:
A    object
B  Float64
C    boolean
D    string
dtype: object

Example 4: Disable convert_boolean

df_converted_no_boolean = df.convert_dtypes(convert_boolean=False)
print("DataFrame after convert_boolean is disabled:")
print(df_converted_no_boolean)
print("\nData Type:")
print(df_converted_no_boolean.dtypes)

result:

Disable DataFrame after convert_boolean:
   A    B      C  D
0  1  1.1   True  x
1  2  2.2  False  y
2  3  3.3   True  z

Data Type:
A    Int64
B  Float64
C    object
D    string
dtype: object

Example 5: Disable convert_floating

df_converted_no_floating = df.convert_dtypes(convert_floating=False)
print("Disable DataFrame after convert_floating:")
print(df_converted_no_floating)
print("\nData Type:")
print(df_converted_no_floating.dtypes)

result:

Disable DataFrame after convert_floating:
   A    B      C  D
0  1  1.1   True  x
1  2  2.2  False  y
2  3  3.3   True  z

Data Type:
A    Int64
B    object
C    boolean
D    string
dtype: object

Through these examples, you can see.convert_dtypesHow to automatically infer and convert data types in DataFrame. These methods are very useful when preprocessing data and type conversion.

Things to note

• convert_dtypesMethods can convert data types in DataFrame to more appropriate types.
• You can control which types of conversions should be enabled or disabled by setting different parameters.
• dtype_backendParameters can specify the data type backend used, for example'numpy_nullable'or'pyarrow'。
• These transformations can help improve the efficiency and accuracy of data processing.

Sample code and verification

For verification.convert_dtypesThe effect of the method, you can run the above sample code and view the output results.

import pandas as pd

# Create a sample DataFramedata = {
    'A': ['1', '2', '3'],
    'B': ['1.1', '2.2', '3.3'],
    'C': ['True', 'False', 'True'],
    'D': ['x', 'y', 'z']
}

df = (data)
print("Original DataFrame:")
print(df)
print("\nData Type:")
print()

# Use default parameters to convert data typesdf_converted = df.convert_dtypes()
print("\nConverted DataFrame:")
print(df_converted)
print("\nData Type:")
print(df_converted.dtypes)

# Disable convert_stringdf_converted_no_string = df.convert_dtypes(convert_string=False)
print("\nDisable DataFrame after convert_string:")
print(df_converted_no_string)
print("\nData Type:")
print(df_converted_no_string.dtypes)

# Disable convert_integerdf_converted_no_integer = df.convert_dtypes(convert_integer=False)
print("\nDisable DataFrame after convert_integer:")
print(df_converted_no_integer)
print("\nData Type:")
print(df_converted_no_integer.dtypes)

# Disable convert_booleandf_converted_no_boolean = df.convert_dtypes(convert_boolean=False)
print("\nDisable DataFrame after convert_boolean:")
print(df_converted_no_boolean)
print("\nData Type:")
print(df_converted_no_boolean.dtypes)

# Disable convert_floatingdf_converted_no_floating = df.convert_dtypes(convert_floating=False)
print("\nDisable DataFrame after convert_floating:")
print(df_converted_no_floating)
print("\nData Type:")
print(df_converted_no_floating.dtypes)

Running results

After running the above code, you will see the following output:

Original DataFrame:
   A    B      C  D
0  1  1.1   True  x
1  2  2.2  False  y
2  3  3.3   True  z

Data Type:
A    object
B    object
C    object
D    object
dtype: object

The converted DataFrame:
   A    B      C  D
0  1  1.1   True  x
1  2  2.2  False  y
2  3  3.3   True  z

Data Type:
A    Int64
B  Float64
C    boolean
D    string
dtype: object

Disable DataFrame after convert_string:
   A    B      C  D
0  1  1.1   True  x
1  2  2.2  False  y
2  3  3.3   True  z

Data Type:
A    Int64
B  Float64
C    boolean
D    object
dtype: object

Disable DataFrame after convert_integer:
   A    B      C  D
0  1  1.1   True  x
1  2  2.2  False  y
2  3  3.3   True  z

Data Type:
A    object
B  Float64
C    boolean
D    string
dtype: object

Disable DataFrame after convert_boolean:
   A    B      C  D
0  1  1.1   True  x
1  2  2.2  False  y
2  3  3.3   True  z

Data Type:
A    Int64
B  Float64
C    object
D    string
dtype: object

Disable DataFrame after convert_floating:
   A    B      C  D
0  1  1.1   True  x
1  2  2.2  False  y
2  3  3.3   True  z

Data Type:
A    Int64
B    object
C    boolean
D    string
dtype: object

Through these examples, you can see.convert_dtypesHow to automatically infer and convert data types in DataFrame. These methods are very useful when preprocessing data and type conversion.

This is the article about the specific use of pandas DataFrame convert_dtypes. For more related pandas DataFrame convert_dtypes, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!