Adventures in Machine Learning

Efficiently Analyzing Numeric Data with Pandas: Selecting and Verifying Columns

1) Selecting only numeric columns in a pandas DataFrame

When dealing with data, it is essential to only focus on data that is relevant to your analysis. For example, if you are analyzing basketball player statistics, it is essential to only select columns that are numeric and relevant to your analysis, such as points scored, rebounds, assists, etc.

Pandas makes selecting numeric columns easy by providing a simple function called select_dtypes(). This function can be used to select only columns of a certain type.

Here’s an example of how to use this function for selecting just numeric columns:

# Import the pandas library
import pandas as pd
# Create a DataFrame of basketball player statistics
data = {'Player Name': ['LeBron James', 'Kobe Bryant', 'Kevin Durant', 'Stephen Curry', 'Michael Jordan'],
        'Age': [28, 35, 29, 32, 30],
        'Points Scored': [30, 25, 23, 28, 32],
        'Rebounds': [10, 7, 6, 5, 8],
        'Assists': [8, 5, 3, 7, 7]}
basketball_df = pd.DataFrame(data)
# Selecting only numeric columns
numeric_columns = basketball_df.select_dtypes(include=['int64', 'float64'])
print(numeric_columns)

In the example above, we have created a basketball player statistics DataFrame using the pd.DataFrame() function. We then used select_dtypes() to select only the numeric columns in the DataFrame, which include the Age, Points Scored, Rebounds, and Assists columns.

Note that in the example above, we have explicitly specified the data types that we want to include using the include parameter. If we wanted to exclude certain data types, we could use the exclude parameter instead.

2) Verifying Numeric Columns in a pandas DataFrame

Once you have selected the numeric columns in a pandas DataFrame, it’s important to verify that the data types of these columns are indeed numeric. This can be especially important if you are using these columns for calculations or mathematical operations, as the wrong data type can yield incorrect results.

To verify the data types of the columns in a DataFrame, you can use the dtypes() function. This function returns a Series with the data type of each DataFrame variable.

Here’s an example:

# Verifying Numeric Columns
print(numeric_columns.dtypes)

The output of this code will be:

Age               int64
Points Scored    int64
Rebounds         int64
Assists          int64
dtype: object

In the output above, we can see that all the numeric columns have data types of int64, which is what we would expect for whole numbers. If a column had a data type of object instead, we would know that it contains string values and is not numeric.

3) Listing Numeric Columns in a pandas DataFrame

Sometimes it can be handy to have a list of all of the numeric columns in a pandas DataFrame. This list can be useful when we want to perform quick analyses or when we want to work with a subset of the numeric columns.

Fortunately, creating a list of numeric columns is quite simple.

# Listing Numeric Columns
numeric_columns_list = numeric_columns.columns.tolist()
print(numeric_columns_list)

In the example above, we are creating a list of all the numeric columns in our basketball_df DataFrame using the .columns.tolist() method. The output of this code will be:

['Age', 'Points Scored', 'Rebounds', 'Assists']

Here, we can see that the list contains all the numeric columns in our DataFrame.

4) Additional Resources

Pandas is a powerful tool for data analysis, but it can also be challenging to learn. Fortunately, there are many resources available to help you learn more about pandas and how to use it effectively.

Here are a few resources that you might find helpful:

  • The Pandas Documentation: The official documentation for Pandas is an excellent resource for learning about pandas. The documentation includes everything from basic tutorials to in-depth explanations of every function and method available in pandas. You can find the documentation here: https://pandas.pydata.org/docs/.
  • Data School: Data School is a popular YouTube channel that has many videos on pandas and data analysis. The channel has a variety of videos that cover everything from basic data visualization to advanced statistical modeling. You can find the Data School channel here: https://www.youtube.com/c/dataschool.
  • Pandas Cookbook: The Pandas Cookbook is a free resource that contains many recipes that show you how to use pandas for various data analysis tasks. The cookbook includes recipes for everything from indexing and selecting data to merging, joining, and reshaping data. You can find the Pandas Cookbook here: https://pandas.pydata.org/pandas-docs/stable/user_guide/cookbook.html.
  • Kaggle: Kaggle is a platform for data scientists to compete in challenges, collaborate on projects, and learn new skills. Kaggle has many pandas tutorials and challenges that you can use to test your skills and learn more about pandas. You can find Kaggle here: https://www.kaggle.com/learn/pandas.
  • DataCamp: DataCamp is an online learning platform that provides courses on pandas, Python, and data analysis. DataCamp’s pandas courses range from beginner to advanced and cover everything from pandas basics to advanced data manipulation techniques. You can find DataCamp here: https://www.datacamp.com/courses/pandas-foundations.

Conclusion:

In conclusion, pandas is a powerful tool for data analysis, and knowing how to manipulate data can be very useful when performing analyses. In this article, we have explored how to select numeric columns in a pandas DataFrame, verify their data types, and list all of the numeric columns in a DataFrame.

We have also provided a few resources that you can use to continue learning more about pandas and data analysis. By employing these techniques and resources, you can make your data analysis more manageable, efficient, and insightful.

In this article, we have explored the topic of selecting and verifying numeric columns in a pandas DataFrame, which is essential for accurate data analysis. By using the select_dtypes() function and the dtypes() function, we can efficiently select and verify the data types of numeric columns in the DataFrame, respectively.

Furthermore, we have shown how to list numeric columns in a DataFrame and provided additional resources for those who want to learn more about pandas and data analysis. All these techniques and resources highlight the importance of data manipulation in performing accurate analyses.

By using pandas, data analysis becomes more efficient, manageable, and insightful. Therefore, it is important to keep these techniques in mind to ensure accurate and efficient data analysis.

Popular Posts