Jupyter Guide

To get started, open the file on the right entitled "Step 1...". You'll complete each task for Step 1 in that Jupyter Notebook file. Remember, you must run the cells (ctrl/cmd + Enter) for each task before moving onto the next task in the Jupyter Notebook. Continue until you have completed all tasks in this step. Then when you are ready to move onto the next step, you'll come back and click on the file for the next step until you have completed all tasks in all steps of the lab.

Exploring DataFrames and Indexing in Pandas

To review the concepts covered in this step, please refer to the Introduction to Indexing Objects in Pandas module of the Index Objects with Pandas course.

Understanding the structure of DataFrames and the concept of indexing in Pandas is important because it forms the foundation for data extraction, manipulation, and modification. This step will allow you to practice the basics of indexing and explore a dataset using position-based indexing.

Let's dive into the world of data analysis with Pandas! In this step, you'll get hands-on experience with the basics of DataFrames and indexing in Pandas. You'll be using the Learning_Management.csv dataset to practice extracting data using numerical indexing, and selecting subsets of data using both row and column labels. The goal here is to familiarize yourself with the structure of DataFrames and understand the importance of indexing in data extraction.

Task 1.1: Importing the Pandas Library

Before you can start working with DataFrames, you need to import the pandas library. Import the pandas library as pd.

import pandas as pd

Task 1.2: Loading the Dataset

Load the Learning_Management.csv file into a DataFrame using pandas. Name the DataFrame df.

df = pd.read_csv('Learning_Management.csv')

Task 1.3: Inspecting the DataFrame

Inspect the first 5 rows of the DataFrame using the head() function.

df.head()

Task 1.4: Selecting a Single Column

Select the employee_name column from the DataFrame.

df['employee_name']

Task 1.5: Selecting Multiple Columns

Select the employee_name and course_name columns from the DataFrame.

df[['employee_name', 'course_name']]

Task 1.6: Selecting Rows Using Index

Select the first 10 rows of the DataFrame using numerical indexing.

df.iloc[:10]

Task 1.7: Selecting Subsets of Data

Select the employee_name and course_name columns for the first 10 rows of the DataFrame.

df.iloc[:10][['employee_name', 'course_name']]

Working with Time Series Data in Pandas

To review the concepts covered in this step, please refer to the Pandas Index Objects for Time Series Data module of the Index Objects with Pandas course.

Understanding how to use datetime and timedelta indexing in Pandas is important because it allows for efficient handling and manipulation of time-series data. This step will provide you with the opportunity to practice these concepts using a real-world dataset.

Time to tackle time-series data! In this step, you'll explore how to use datetime and timedelta indexing in Pandas to manipulate and extract data. Using the completion_date column from the Learning_Management.csv dataset, you'll practice creating a datetime index, extracting data for specific time periods, and performing basic operations on pandas built-in datetime objects. The goal is to get comfortable with handling time-series data in Pandas.

Task 2.1: Load the Dataset

Start by loading the Learning_Management.csv dataset into a pandas DataFrame. Name the DataFrame df.

After loading the data, display the head of the DataFrame to view the first few rows.

import pandas as pd

df = pd.read_csv('Learning_Management.csv')
df.head()

Task 2.2: Convert the 'completion_date' Column to Datetime

Convert the 'completion_date' column in the DataFrame to a datetime object. This will allow you to perform time-series operations on the data.

After converting, print the data type of the column again to see the change.

# Provided code
print(df['completion_date'].dtype)

# Convert the 'completion_date' column to datetime and print column dtype
df['completion_date'] = pd.to_datetime(df['completion_date'])
print(df['completion_date'].dtype)

Task 2.3: Set the 'completion_date' Column as the DataFrame Index

Set the 'completion_date' column as the index of the DataFrame. This will allow you to use datetime indexing to select data based on the completion date.

After setting the index, display the head of the DataFrame to see the changes.

df = df.set_index('completion_date')
df.head()

Task 2.4: Select Data for a Specific Time Period

Select all rows in the DataFrame where the completion date is in May 2022.

After selecting, display the selected data to verify the result.

may_2022_data = df.loc['2022-05']
may_2022_data

Task 2.5: Perform a Basic Operation on a Datetime Object

Calculate the number of days between the earliest and latest completion dates in the DataFrame.

Display the result to see the number of days.

num_days = df.index.max() - df.index.min()
num_days

Interval, Categorical, and Period Indexing in Pandas

To review the concepts covered in this step, please refer to the Interval, Categorical, and Period Indexing in Pandas module of the Index Objects with Pandas course.

Understanding how to create and use interval, categorical, and period indices in Pandas is important because these indexing techniques enable advanced data extraction from a DataFrame. This step will allow you to practice creating these indices and using them to extract data from a DataFrame.

Ready to level up your indexing skills? In this step, you'll delve into interval, categorical, and period indexing in Pandas. You'll practice creating these indices using sample data and learn how to use them for efficient data extraction. The goal is to practice these advanced indexing techniques for more efficient data extraction.

Task 3.1: Create an Interval Index

Create an interval index based on a range of values. Use the pd.cut() function to divide the range 0 to 100 into 5 equal intervals. This method helps in binning or bucketing the data.

# Provided Code
import pandas as pd
import numpy as np

interval_index = pd.cut(range(0, 101), 5)

Task 3.2: Create a DataFrame Using Interval Index

Use the interval index created in Task 3.1 to create a DataFrame with random data. The DataFrame should have 101 rows and 2 columns. Use the provided code to create the random data.

# Provided code
random_data = np.random.randn(101, 2)

df_interval = pd.DataFrame(random_data, index=interval_index, columns=['A', 'B'])

Task 3.3: Index into the Interval Indexed DataFrame

Select the rows from the DataFrame created in Task 3.2 where the interval index includes the value 42.

df_interval.loc[42]

Task 3.4: Create a Categorical Index

Create a categorical index using a list of 4 categories. Categorical data is a Pandas data type corresponding to categorical variables in statistics.

categories = ['Category1', 'Category2', 'Category3', 'Category4']
categorical_index = pd.Categorical(categories)

Task 3.5: Create a DataFrame Using Categorical Index

Use the categorical index created in Task 3.4 to create a DataFrame with random data. The DataFrame should have 4 rows and 2 columns. Use the provided code to create the random data.

# Provided code
random_data = np.random.randn(4, 2)

df_categorical = pd.DataFrame(random_data, index=categorical_index, columns=['A', 'B'])

Task 3.6: Index into the Categorical Indexed DataFrame

Select the row from the DataFrame created in Task 3.5 that corresponds to your second category.

df_categorical.loc['Category2']

Task 3.7: Create a Period Index

Create a period index representing each month in 2023. Period indices are useful for time series data that require to be aggregated or indexed by a particular time period.

period_index = pd.period_range('2023-01', periods=12, freq='M')

Task 3.8: Create a DataFrame Using Period Index

Use the period index created in Task 3.7 to create a DataFrame with random data. The DataFrame should have 12 rows and 2 columns.

# Provided code
random_data = np.random.randn(12, 2)

df_period = pd.DataFrame(random_data, index=period_index, columns=['A', 'B'])

Task 3.9: Index into the Period Indexed DataFrame

Select the row from the DataFrame created in Task 3.8 that corresponds to the month '2023-05'.

df_period.loc['2023-05']

Multi-indexing in Pandas

To review the concepts covered in this step, please refer to the Multi-indexing in Pandas module of the Index Objects with Pandas course.

Understanding how to create and use a MultiIndex in Pandas is important because it allows for efficient organization and retrieval of hierarchical data. This step will provide you with the opportunity to practice creating a MultiIndex and using it to retrieve data at different hierarchy levels.

Let's dive into the world of multi-indexing! In this step, you'll learn how to create a MultiIndex for hierarchical data organization in a DataFrame. Using the Learning_Management.csv dataset, you'll practice creating a MultiIndex and using it to retrieve data at different hierarchy levels. The goal is to understand the benefits of using MultiIndexing in pandas for hierarchical data organization and efficient data retrieval.

Task 4.1: Importing Pandas

Before we start working with the data, we need to import pandas. In this task, import the pandas library which will be used throughout this step.

import pandas as pd

Task 4.2: Loading the Dataset

Now that we have imported pandas, let's load the dataset. The dataset is stored in a CSV file named 'Learning_Management.csv'.

df = pd.read_csv('Learning_Management.csv')

Task 4.3: Creating a MultiIndex

Now that we have loaded the dataset, let's create a MultiIndex. We will use the 'employee_id' and 'course_id' columns as our index. This will allow us to organize our data hierarchically.

After creating the MultiIndex, display the head of the DataFrame to visualize the change.

df.set_index(['employee_id', 'course_id'], inplace=True)
df.head()
# or
# df = df.set_index(['employee_id', 'course_id'])
# df.head()

Task 4.4: Retrieving Data Using MultiIndex

With the MultiIndex created, your next task is to retrieve data for a specific course. Find all employees who completed the course with courseid 'C002'.

df.xs('C002', level='course_id')