Adventures in Machine Learning

Efficiently Analyzing Numeric Data with Pandas: Selecting and Verifying Columns

If youve ever worked with data, you know how important it is to analyze it in a way that makes it easy to understand and interpret. One of the most powerful tools for doing this is pandas, a popular Python library for data analysis that allows you to manipulate tabular data in a variety of ways.

One such task is selecting numeric columns, which can be useful when analyzing numerical data such as sales figures, sports data, or other numerical data sets. In this article, we will explore how to select numeric columns in a pandas DataFrame and verify their data types.

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.

Heres an example of how to use this function for selecting just numeric columns:

“`python

# 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, its 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.

Heres an example:

“`python

# 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.

Conclusion:

In conclusion, selecting the right data for your analysis and verifying that the data types are correct are essential steps in the data analysis process. To select only numeric columns in a pandas DataFrame, you can use the `select_dtypes()` function, and to verify the data type of each variable in the DataFrame, you can use the `dtypes()` function.

These simple commands can help you quickly narrow your focus to the data that you need and ensure that your calculations are accurate. By using pandas for data manipulation, you can make analyzing data more manageable and efficient.

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. “`python

# 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:

1. 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/.

2. 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.

3. 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.

4. 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.

5. DataCamp: DataCamp is an online learning platform that provides courses on pandas, Python, and data analysis.

DataCamps 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.

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