💡 Problem Formulation: When working with datasets in Python, particularly with pandas DataFrames, a common need is to filter columns by specific datatypes. For example, you might have a DataFrame containing strings, dates, and numeric data and you want to select only the numeric data for analysis. Desired output would be a DataFrame containing columns with only the chosen datatype(s).
Method 1: Using select_dtypes() Method
Selecting columns in a DataFrame based on datatypes is straightforward with the select_dtypes() method offered by pandas. This method allows you to specify the datatypes you are interested in and returns a new DataFrame containing just those columns. The specification can be a single datatype or a list of datatypes.
Here’s an example:
import pandas as pd
# Creating a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': ["one", "two", "three"], 'C': [1.1, 2.2, 3.3]})
# Selecting columns with numeric datatypes
numeric_df = df.select_dtypes(include=['number'])
print(numeric_df)
Output:
A C 0 1 1.1 1 2 2.2 2 3 3.3
This code snippet creates a DataFrame with columns of different datatypes and then uses select_dtypes() to select columns with numeric datatypes (‘number’ is a generic type that includes integers, floats, etc.). The resulting DataFrame, numeric_df, contains only the numeric columns from the original DataFrame.
Method 2: Using Data Types Directly
Another method to filter columns by specific datatype involves directly accessing the dtypes attribute of the DataFrame, and then using boolean indexing to select the appropriate columns.
Here’s an example:
import pandas as pd
# Creating a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': ["one", "two", "three"], 'C': [1.1, 2.2, 3.3]})
# Selecting columns of float64 datatype
float_columns = df.columns[df.dtypes == 'float64']
float_df = df[float_columns]
print(float_df)
Output:
C 0 1.1 1 2.2 2 3.3
Here, df.dtypes returns a Series with datatypes of each column. A boolean index is created comparing the datatypes to ‘float64’, and this index is used to filter the DataFrame columns. The output is a DataFrame with only the float columns.
Method 3: Using List Comprehension
A more Pythonic approach might be to use a list comprehension to select column names with a specific datatype. This method is concise and highly readable if you are familiar with list comprehensions.
Here’s an example:
import pandas as pd
# Creating a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': ["one", "two", "three"], 'C': [1.1, 2.2, 3.3]})
# Selecting columns of integer datatype
int_columns = [col for col in df if df[col].dtype == 'int64']
int_df = df[int_columns]
print(int_df)
Output:
A 0 1 1 2 2 3
In this code, a list comprehension is used to filter column names where the datatype of the column data matches ‘int64’. The list of column names is then used to index into the original DataFrame to create int_df, a DataFrame containing only the integer columns.
Method 4: Using a Custom Function
If you need more control or want to encapsulate the logic for reusability, creating a custom function to filter DataFrame columns by datatype can be advantageous.
Here’s an example:
import pandas as pd
# Creating a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': ["one", "two", "three"], 'C': [1.1, 2.2, 3.3]})
# Defining a custom function to select columns by datatype
def select_by_dtype(dataframe, dtype):
return dataframe.select_dtypes(include=[dtype])
# Using the custom function to select 'object' (string) columns
string_df = select_by_dtype(df, 'object')
print(string_df)
Output:
B 0 one 1 two 2 three
This custom function select_by_dtype uses the built-in select_dtypes() method. It’s versatile and can be used across different DataFrames to select columns of any specified datatype, as demonstrated with extracting ‘object’ datatypes in the DataFrame.
Bonus One-Liner Method 5: Using filter() with Regex
For advanced users, the DataFrame’s filter() method can be paired with regular expressions to quickly select columns with specific datatype patterns.
Here’s an example:
import pandas as pd
# Creating a sample DataFrame
df = pd.DataFrame({'A_int': [1, 2, 3], 'B_str': ["one", "two", "three"], 'C_float': [1.1, 2.2, 3.3]})
# Selecting columns where the datatype is in the column label (
regex_df = df.filter(regex='_str$')
print(regex_df)
Output:
B_str 0 one 1 two 2 three
In this creative approach, each column name includes a suffix representing its datatype (e.g., ‘_int’, ‘_str’). The filter() method is then used with a regular expression to match the datatype suffix that you’re interested in. Note that this requires a consistent naming convention within the DataFrame.
Summary/Discussion
- Method 1:
select_dtypes(). Strengths: Built-in, simple, can include or exclude multiple datatypes. Weaknesses: Requires knowledge of pandas dtype syntax. - Method 2: Using Data Types Directly. Strengths: Direct access to DataFrame properties, no additional method calls. Weaknesses: Slightly less readable, more verbose than
select_dtypes(). - Method 3: Using List Comprehension. Strengths: Pythonic, concise. Weaknesses: Less intuitive for non-Python programmers, not chainable with other DataFrame methods.
- Method 4: Using a Custom Function. Strengths: Reusable, encapsulates functionality. Weaknesses: Requires additional code, might be unnecessary for simple tasks.
- Method 5: Using
filter()with Regex. Strengths: Extremely powerful and concise. Weaknesses: Dependency on column naming conventions, requires regex knowledge.