The Power of Pandas: Unlocking Data Analysis with Seaborn and Python

As data scientists, we are often faced with the challenge of performing complex analysis on large datasets. One popular tool that helps us achieve this is pandas, a Python library for data analysis. In this article, we will explore how to use pandas in conjunction with Seaborn to gain insights from our data.

Pandas: A Powerful Data Analysis Library

Pandas is a versatile and powerful library that can easily read datasets from many types of files, including CSV and text files. One of the most common data structures used in pandas is the DataFrame object. When we import a dataset using pandas, we create a DataFrame, which is essentially a table of data with rows and columns.

Using Pandas to Create DataFrames

To demonstrate how to use pandas, let's start by importing the library as PD. We will then use the read_csv function to read in a CSV file named masculinity.csv and create a pandas DataFrame called DF. The head() function is used to display the first five rows of the DataFrame.

The dataset we are working with contains the results of a survey of adult men. It has four columns: participant ID, age, how masculine (the person's response to the question "how masculine or manly do you feel"), and how important (the response to the question "how important is it to you that others see you as masculine"). By examining the first five rows of the DataFrame, we can see that each row represents a survey response with one answer to each survey question.

Creating Count Plots with DataFrames

Now that we have our data in a pandas DataFrame, let's learn how to create count plots. In contrast to using a list of data, where we pass in values for the column, we use the name of the column in the DataFrame as X equal to the name of the column. We then set the data parameter equal to our DataFrame DF.

When we call plot(), followed by show(), we see a nice count plot of the values in the how masculine column of our data. This plot shows us that "somewhat" is the most common response, with "very" being the second most common response. Notice also that because we are using a named column in the DataFrame, Seaborn automatically adds the name of the column as the x-axis label at the bottom.

Tidy Data: A Requirement for Seaborn

An important note to make here is that Seaborn works great with pandas dataframes, but only if the data frame is tidy. Tidy data means that each observation has its own row and each variable has its own column. The masculinity DataFrame shown here is tidy because each row represents a survey response with one answer to each survey question in each column.

In contrast, this is an example of an untidy data frame made from the same survey on masculinity. Notice how each row doesn't contain the same information. Row zero contains age categories, rows 1 and 7 contain the question text, and the other rows contain summary data of the responses. This type of data will not work well with Seaborn.

Transforming Untidy Data Frames

While it's possible to transform untidy data frames into tidy ones, this is not a scope for this course. However, there are other DataCamp courses that can teach you how to do this.

Now that we've learned the basics of using pandas and Seaborn, let's move on to our next section:

"WEBVTTKind: captionsLanguage: endata scientists commonly use pandas to perform data analysis so it's a huge advantage that Seabourn works extremely well with pandas data structures let's see how this works pandas is a Python library for data analysis it can easily read datasets from many types of files including CSV and txt files pandas affords several types of data structures but the most common one is the data frame object when you read in a data set with pandas you will create a data frame let's look at an example first import the pandas library as PD then use the read CSV function to read the CSV file named masculinity dot CSV and create a panda's data frame called DF calling head-on the data frame will show us its first five rows this dataset contains the result of a survey of adult men we can see that it has four columns participant ID age how masculine which is that person's response to the question how masculine or manly do you feel and how important which is the response of the question how important is it to you that others see you as masculine now let's take a look at how to make a count plot with a data frame instead of a list of data the first thing we'll do is import pandas that plot Lin and C born as we have in past examples then we'll create a panda's data frame called DF from the masculinity CSV file to create a count plot with a pandas dataframe column instead of a list of data set X equal to the name of the column in the data frame in this case we'll use the how masculine column then we'll set the data parameter equal to our data frame DF after calling plot dot show we can see that we have a nice count plot of the values in the how masculine column of our data this plot shows us the most common response to the question how masculine or manly do you feel is somewhat with very being the second most common response note also that because we're using a named column in the data frame Seabourn automatically adds the name of the column that's the x-axis label at the bottom let's pause for an important note here Seaborn works great with pandas dataframes but only if the data frame is tidy tidy data means that each observation has its own row and each variable has its own column the masculinity data frame shown here is tidy because each row is a survey response with one answer to each survey question in each column making a count plot with the how masculine column works just like passing in a list of that columns values in contrast here is an example of an untidy data frame made from the same survey on masculinity in this untidy data frame notice how each row doesn't contain the same information row zero contains the age categories rows 1 & 7 contain the question text and the other rows contain summary data of the responses this will not work well with Seabourn unlike the tidy data frame values in the age column don't look like a list of age categories for each observation transforming untidy data frames into tidy ones is possible but it's not scope for this course there are other data camp courses that can teach you how to do this now it's time to try out using paneldata scientists commonly use pandas to perform data analysis so it's a huge advantage that Seabourn works extremely well with pandas data structures let's see how this works pandas is a Python library for data analysis it can easily read datasets from many types of files including CSV and txt files pandas affords several types of data structures but the most common one is the data frame object when you read in a data set with pandas you will create a data frame let's look at an example first import the pandas library as PD then use the read CSV function to read the CSV file named masculinity dot CSV and create a panda's data frame called DF calling head-on the data frame will show us its first five rows this dataset contains the result of a survey of adult men we can see that it has four columns participant ID age how masculine which is that person's response to the question how masculine or manly do you feel and how important which is the response of the question how important is it to you that others see you as masculine now let's take a look at how to make a count plot with a data frame instead of a list of data the first thing we'll do is import pandas that plot Lin and C born as we have in past examples then we'll create a panda's data frame called DF from the masculinity CSV file to create a count plot with a pandas dataframe column instead of a list of data set X equal to the name of the column in the data frame in this case we'll use the how masculine column then we'll set the data parameter equal to our data frame DF after calling plot dot show we can see that we have a nice count plot of the values in the how masculine column of our data this plot shows us the most common response to the question how masculine or manly do you feel is somewhat with very being the second most common response note also that because we're using a named column in the data frame Seabourn automatically adds the name of the column that's the x-axis label at the bottom let's pause for an important note here Seaborn works great with pandas dataframes but only if the data frame is tidy tidy data means that each observation has its own row and each variable has its own column the masculinity data frame shown here is tidy because each row is a survey response with one answer to each survey question in each column making a count plot with the how masculine column works just like passing in a list of that columns values in contrast here is an example of an untidy data frame made from the same survey on masculinity in this untidy data frame notice how each row doesn't contain the same information row zero contains the age categories rows 1 & 7 contain the question text and the other rows contain summary data of the responses this will not work well with Seabourn unlike the tidy data frame values in the age column don't look like a list of age categories for each observation transforming untidy data frames into tidy ones is possible but it's not scope for this course there are other data camp courses that can teach you how to do this now it's time to try out using panel\n"