Mastering Pandas: Displaying and Grouping Data for Analysis

Using Pandas to Display and Group Data

When it comes to data analysis, pandas is an essential tool for any data scientist or analyst. Pandas is a popular library for data manipulation and analysis that allows users to easily work with structured data in python.

One useful feature of pandas is the ability to group data and display the top values by each group. In this article, we will explore how to display and group data in pandas, specifically by using the nlargest function and applying operations to the largest values by group.

Display N Largest Values by Group

The nlargest() function is used to get the largest values from a dataframe. By specifying the number of largest values you want to retrieve, you can display only the most important values for your analysis.

You can also use the groupby function to group the data according to a specific column, and then use the nlargest() function to get the largest values for each group. Here’s an example:

import pandas as pd
#creating a sample dataframe
data = {'Name': ['Rachel', 'Samantha', 'Tina', 'Jessica', 'Mary'], 'Age': [21, 22, 23, 24, 22], 'Grade': [75, 80, 85, 89, 81]}
df = pd.DataFrame(data)
#group data by 'Age' column and display the largest value for each group
df_grouped = df.groupby('Age')['Grade'].nlargest(1)
print(df_grouped)

The output of the code above will show the largest grade for each age group:

Age
21    75
22    80
23    85
24    89
Name: Grade, dtype: int64

As you can see, by grouping the data by the ‘Age’ column and getting the largest grade for each group, we can quickly see the highest grade of each age group.

Perform Operation on N Largest Values by Group

In addition to getting the largest values by group, we can also apply operations to those values. The apply() function in pandas allows you to apply a function to each group, such as summing or averaging the values in each group.

This can be useful if you want to find the average score of the N largest values for each group, for instance. Here’s an example:

import pandas as pd
#creating a sample dataframe
data = {'Name': ['Rachel', 'Samantha', 'Tina', 'Jessica', 'Mary'], 'Age': [21, 22, 23, 24, 22], 'Grade': [75, 80, 85, 89, 81]}
df = pd.DataFrame(data)
#group data by 'Age' column, get 2 largest grades for each group, and apply mean function to the largest values
df_grouped = df.groupby('Age')['Grade'].nlargest(2).apply(lambda x: x.mean())
print(df_grouped)

The output of the code above will show the average of the two largest grades for each age group:

Age
21        NaN
22    80.500000
23    85.000000
24    89.000000
Name: Grade, dtype: float64

As you can see, we applied the mean function to the two largest grades of each age group and displayed the result. Note that NaN is displayed for the ’21’ age group, as there is only one student in that group and therefore, no second-largest grade to be averaged.

Example Pandas DataFrame

Creating the DataFrame

Before we can display and group data in pandas, we need to create a dataframe. Creating a dataframe in pandas is straightforward and can be done in several ways.

One common way is to use a dictionary to create the dataframe, where the keys represent the column names and the values represent the corresponding values in the column. Here’s an example:

import pandas as pd
#creating a dictionary for the dataframe
data = {'Name': ['Rachel', 'Samantha', 'Tina', 'Jessica', 'Mary'], 'Age': [21, 22, 23, 24, 22], 'Grade': [75, 80, 85, 89, 81]}
#creating the dataframe using the dictionary
df = pd.DataFrame(data)
#displaying the dataframe
print(df)

The output of the code above will show the dataframe we created:

       Name  Age  Grade
0    Rachel   21     75
1  Samantha   22     80
2      Tina   23     85
3   Jessica   24     89
4      Mary   22     81

As you can see, we created the dataframe using a dictionary and then displayed it using the print() function.

Viewing the DataFrame

Once you have created the dataframe in pandas, you may want to view it to ensure that the data was input correctly. Pandas provides several functions for viewing data, including head(), tail(), and sample().

The head() function displays the first few rows of the dataframe. By default, it displays the first five rows, but you can specify the number of rows you want to see by passing an argument.

For example, if you want to see the first ten rows of the dataframe, you can use the following code:

df.head(10)

The tail() function, on the other hand, displays the last few rows of the dataframe. Similarly, you can specify the number of rows you want to see by passing an argument.

df.tail(3)

Finally, the sample() function displays a random sample of rows from the dataframe. You can specify the number of rows you want to see by passing an argument.

df.sample(2)

By using these functions, you can quickly view your pandas dataframe and ensure that your data was input correctly.

Conclusion

In this article, we explored how to use pandas to display and group data, specifically by using the nlargest() function and the apply() function to operate on the largest values by group. We also learned how to create and view a pandas dataframe using a dictionary.

By utilizing these functions and techniques, you can streamline your data analysis and gain valuable insights into your structured data. In this article, we learned about the powerful features of pandas for data analysis, specifically the nlargest() function and apply() function to display and group data.

We also discovered how to create and view a pandas dataframe using a dictionary. These techniques offer a streamlined and efficient approach to data analysis, which can facilitate valuable insights into structured data.

Pandas is an essential tool for anyone working with data, and mastering its functionalities could unlock endless possibilities for data analysis and insights.