Introduction to DataFrames in Pandas
Overview of Pandas and DataFrames
Pandas is a powerful Python library used for data manipulation and analysis. It provides high-performance, easy-to-use data structures and data analysis tools. One of the core data structures in pandas is the DataFrame, which is a two-dimensional table of data with columns of potentially different types.
A DataFrame is similar to an Excel spreadsheet or a SQL table. Each column in a DataFrame represents a variable, and each row represents a single observation. DataFrames are ideal for storing and manipulating datasets that contain multiple variables and observations.
Creating a DataFrame
To create a DataFrame in pandas, you can use the pd.DataFrame function, which takes a dictionary-like object as input, where each key corresponds to a column name and each value is the corresponding data.
Here’s an example of creating a simple DataFrame:
import pandas as pd
# Create a dictionary with data
data = {'Name': ['John', 'Anna', 'Peter', 'Linda'],
'Age': [28, 24, 35, 32],
'Country': ['USA', 'UK', 'Australia', 'Germany']}
# Create a DataFrame from the dictionary
df = pd.DataFrame(data)
print(df)
Output:
Name Age Country
0 John 28 USA
1 Anna 24 UK
2 Peter 35 Australia
3 Linda 32 Germany
Displaying and Adding a Column to a DataFrame
In the original question, the user created a DataFrame called date using the following code:
date = data.groupby(pd.to_datetime(data['Completion Date'], format='%d.%m.%Y').dt.month)['Learning Hours'].sum()
date.index = pd.to_datetime(date.index, format='%m').month_name().str[:3]
date.rename_axis('Month').reset_index(name='Learning hours')
However, the user was unable to call the date DataFrame by its name in other cells.
To fix this issue, we need to assign the output back to the date variable and then create a new column.
Assigning Output Back to the date Variable
The first step is to reassign the output of the groupby operation back to the date variable:
date = data.groupby(pd.to_datetime(data['Completion Date'], format='%d.%m.%Y').dt.month)['Learning Hours'].sum()
This will update the date variable with the new values.
Creating a New Column
Next, we can create a new column called Required with values 430 in all rows:
date['Required'] = 430
However, this code assumes that the date DataFrame is already created. If you want to add this step to the original code, you would need to reassign the output of the groupby operation back to the date variable.
Alternative Solutions
The user suggested alternative solutions using different methods:
- Using the
renamemethod:
months = (pd.to_datetime(data['Completion Date'], format='%d.%m.%Y')
.dt.strftime('%b')
.rename('Month'))
date = (data.groupby(months, sort=False)['Learning Hours']
.sum()
.reset_index(name='Learning hours'))
date['Required'] = 430
- Using the
assignmethod:
months = (pd.to_datetime(data['Completion Date'], format='%d.%m.%Y')
.dt.strftime('%b')
.rename('Month'))
date = (data.groupby(months, sort=False)['Learning Hours']
.sum()
.reset_index(name='Learning hours')
.assign(Required = 430))
These solutions use the groupby method with a different approach to create the date DataFrame.
Conclusion
In this article, we discussed how to display and add a column to a DataFrame in pandas. We covered the basics of DataFrames, including creating and manipulating data using the pd.DataFrame function. We also presented alternative solutions for adding a new column to an existing DataFrame.
Code Blocks
Creating a DataFrame
import pandas as pd
# Create a dictionary with data
data = {'Name': ['John', 'Anna', 'Peter', 'Linda'],
'Age': [28, 24, 35, 32],
'Country': ['USA', 'UK', 'Australia', 'Germany']}
# Create a DataFrame from the dictionary
df = pd.DataFrame(data)
print(df)
Grouping Data and Adding a Column
import pandas as pd
# Create a DataFrame
data = {'Name': ['John', 'Anna', 'Peter', 'Linda'],
'Age': [28, 24, 35, 32],
'Country': ['USA', 'UK', 'Australia', 'Germany']}
df = pd.DataFrame(data)
# Group data and add a column
date = df.groupby(df['Country'].dt.month)['Age'].sum().reset_index()
date.rename(columns={'Age':'Total_Age'},inplace=True)
date['Required'] = 430
print(date)
Using Alternative Solutions
import pandas as pd
# Create a DataFrame
data = {'Name': ['John', 'Anna', 'Peter', 'Linda'],
'Age': [28, 24, 35, 32],
'Country': ['USA', 'UK', 'Australia', 'Germany']}
df = pd.DataFrame(data)
# Group data and add a column using rename
months = (pd.to_datetime(df['Country'].dt.strftime('%b')).rename('Month'))
date = df.groupby(months, sort=False)['Age'].sum().reset_index()
date.rename(columns={'Age':'Total_Age'},inplace=True)
date['Required'] = 430
print(date)
# Group data and add a column using assign
months = (pd.to_datetime(df['Country'].dt.strftime('%b')).rename('Month'))
date = df.groupby(months, sort=False)['Age'].sum().reset_index()
date.assign(Required=430)
print(date)
Note that I have used the rename method to rename the column ‘Total_Age’ in this code block.
Last modified on 2023-09-12