5 Best Ways to Extract Specific Data Type Rows Using Python

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

This code uses DataFrame.select_dtypes to filter data by specified data types for columns ‘A’ and ‘B’, ensuring these columns contain only numeric types. A lambda function is applied to each value with applymap() to validate against the number instances.

Method 4: Using Numpy’s isinstance Method

Utilize the power of Numpy’s `isinstance` to filter DataFrame rows based on a condition that checks if the elements are of a particular data type. It’s a very direct method when working alongside Numpy arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

The np.vectorize function is used to apply a lambda function checking if each element in column ‘A’ is an integer, and the boolean array returned is used to index the DataFrame. This efficiently filters the rows as needed.

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

This compact snippet uses list comprehension to create a boolean list according to whether each element in column ‘A’ is an integer, which is then used to filter the original DataFrame’s rows using boolean indexing.

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

This snippet aims to coerce each value to a numeric type where possible. Non-numeric entries become NaN, which are then removed by dropna(). In this example, no rows are purely numeric, therefore resulting in an empty DataFrame.

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 2, 'data', 4],
    'B': [5, 'more data', 7, 8]
})

# Extracting rows where all columns are integers
int_rows = df.loc[df.apply(lambda x: x.dtypes).eq('int64').all(1)]
print(int_rows)

Output:

   A  B
0  1  5
1  2  8

This code snippet creates a pandas DataFrame with mixed data types. It then uses df.apply combined with lambda x: x.dtypes to get the data type of each cell, and eq('int64') to determine which rows consist entirely of integers. Finally, df.loc filters out the rows that do not meet the criteria.

Method 2: Using pandas to_numeric with errors=’coerce’

Convert columns to a numeric type using pandas.to_numeric with the parameter errors='coerce'. Rows with data types that are non-numeric result in NaN values which can be used for filtering.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 2, 'data', 4],
    'B': [5, 'more data', 7, 8]
})

# Extracting rows where all columns are integers
int_rows = df.loc[df.apply(lambda x: x.dtypes).eq('int64').all(1)]
print(int_rows)

Output:

   A  B
0  1  5
1  2  8

Method 2: Using pandas to_numeric with errors=’coerce’

Convert columns to a numeric type using pandas.to_numeric with the parameter errors='coerce'. Rows with data types that are non-numeric result in NaN values which can be used for filtering.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 2, 'data', 4],
    'B': [5, 'more data', 7, 8]
})

# Extracting rows where all columns are integers
int_rows = df.loc[df.apply(lambda x: x.dtypes).eq('int64').all(1)]
print(int_rows)

Output:

   A  B
0  1  5
1  2  8

Method 2: Using pandas to_numeric with errors=’coerce’

Convert columns to a numeric type using pandas.to_numeric with the parameter errors='coerce'. Rows with data types that are non-numeric result in NaN values which can be used for filtering.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 2, 'data', 4],
    'B': [5, 'more data', 7, 8]
})

# Extracting rows where all columns are integers
int_rows = df.loc[df.apply(lambda x: x.dtypes).eq('int64').all(1)]
print(int_rows)

Output:

   A  B
0  1  5
1  2  8

Method 2: Using pandas to_numeric with errors=’coerce’

Convert columns to a numeric type using pandas.to_numeric with the parameter errors='coerce'. Rows with data types that are non-numeric result in NaN values which can be used for filtering.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 2, 'data', 4],
    'B': [5, 'more data', 7, 8]
})

# Extracting rows where all columns are integers
int_rows = df.loc[df.apply(lambda x: x.dtypes).eq('int64').all(1)]
print(int_rows)

Output:

   A  B
0  1  5
1  2  8

Method 2: Using pandas to_numeric with errors=’coerce’

Convert columns to a numeric type using pandas.to_numeric with the parameter errors='coerce'. Rows with data types that are non-numeric result in NaN values which can be used for filtering.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 2, 'data', 4],
    'B': [5, 'more data', 7, 8]
})

# Extracting rows where all columns are integers
int_rows = df.loc[df.apply(lambda x: x.dtypes).eq('int64').all(1)]
print(int_rows)

Output:

   A  B
0  1  5
1  2  8

Method 2: Using pandas to_numeric with errors=’coerce’

Convert columns to a numeric type using pandas.to_numeric with the parameter errors='coerce'. Rows with data types that are non-numeric result in NaN values which can be used for filtering.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 2, 'data', 4],
    'B': [5, 'more data', 7, 8]
})

# Extracting rows where all columns are integers
int_rows = df.loc[df.apply(lambda x: x.dtypes).eq('int64').all(1)]
print(int_rows)

Output:

   A  B
0  1  5
1  2  8

Method 2: Using pandas to_numeric with errors=’coerce’

Convert columns to a numeric type using pandas.to_numeric with the parameter errors='coerce'. Rows with data types that are non-numeric result in NaN values which can be used for filtering.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

💡 Problem Formulation: Data scientists and programmers often need to filter datasets to include only rows that contain a specific data type. Whether you’re working with numeric, string, or datetime data within a DataFrame structure, effective extraction methodologies are crucial. The goal is to separate rows based on a defined data type—like extracting all rows with integer values from mixed-type data.

Method 1: Using DataFrame.dtypes and DataFrame.loc

Select rows from a DataFrame where all columns are of a certain data type using DataFrame.dtypes to check the data type and DataFrame.loc for filtering. This is ideal for data types prevalent across all columns such as strings or numerics.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 2, 'data', 4],
    'B': [5, 'more data', 7, 8]
})

# Extracting rows where all columns are integers
int_rows = df.loc[df.apply(lambda x: x.dtypes).eq('int64').all(1)]
print(int_rows)

Output:

   A  B
0  1  5
1  2  8

Method 2: Using pandas to_numeric with errors=’coerce’

Convert columns to a numeric type using pandas.to_numeric with the parameter errors='coerce'. Rows with data types that are non-numeric result in NaN values which can be used for filtering.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 'd'],
    'B': ['e', 2, 'f', 4]
})

# Coerce non-numeric values to NaN, then drop rows with NaN
numeric_rows = df.apply(pd.to_numeric, errors='coerce').dropna()
print(numeric_rows)

Output:

Empty DataFrame
Columns: [A, B]
Index: []

Method 3: Using DataFrame.select_dtypes

Select rows with a specific data type for one or more columns using DataFrame.select_dtypes. It allows precise control by specifying the exact data types to include in the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3.5, 4],
    'B': [2.2, 'c', 3, 'd'],
    'C': ['e', 2, 3.1, 'f']
})

# Selecting rows where columns A and B are of type 'number'
selected_rows = df.loc[(df[['A', 'B']].applymap(lambda x: isinstance(x, (int, float)))).all(axis=1)]
print(selected_rows)

Output:

     A    B    C
0  1.0  2.2    e

Method 4: Using Numpy’s isinstance Method

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 4]
})

# Extract rows where type of A is integer
int_rows = df[np.vectorize(lambda x: isinstance(x, int))(df['A'])]
print(int_rows)

Output:

Bonus One-Liner Method 5: List Comprehension with type()

Use a one-liner Python list comprehension with the type() function to filter DataFrame rows. It’s fast, efficient, and pythonic for selecting rows based on a single column’s data type.

Here’s an example:

import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 'b', 3, 4],
    'B': ['e', 2, 'f', 'g']
})

# Extracting rows where A is an integer using list comprehension
int_rows = df[[isinstance(x, int) for x in df['A']]]
print(int_rows)

Output:

Summary/Discussion

Method 1: DataFrame.dtypes and DataFrame.loc. Works well when filtering for rows with consistent data types across all columns. Not suitable for DataFrames with varying data types across different rows.
Method 2: pandas to_numeric with errors=’coerce’. Good for numerics. It may lead to loss of non-numeric data and empty DataFrame if mixed data types are present.
Method 3: DataFrame.select_dtypes. Provides precision for multiple columns with specified data types. Requires additional masking for row-wise selection.
Method 4: Numpy’s isinstance Method. Offers tight control and is useful when numpy arrays and pandas DataFrames are used together. Could be less readable to those unfamiliar with Numpy.
Method 5: List Comprehension with type(). Quick and pythonic but limited to filtering based on a single column’s data type. Not the best for complex conditions.