Best Python Data Analysis Books to Buy in October 2025
Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter
Data Analysis with Python and PySpark
Python for Data Science: A Hands-On Introduction
Murach's Python for Data Analysis (Training & Reference)
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Python for Excel: A Modern Environment for Automation and Data Analysis
Pandas Cookbook: Practical recipes for scientific computing, time series, and exploratory data analysis using Python
Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python, 3rd Edition
In pandas, you can assign new columns based on chaining by using the .assign() method. This method allows you to add new columns to a DataFrame by specifying the column name and the values for the new column.
For example, you can chain multiple .assign() calls together to create multiple new columns in one go. This can be achieved by using the assignment operator (=) to assign new values to the existing columns or create new columns based on the existing ones.
Additionally, you can also use lambda functions or other custom functions to create the values for the new columns based on the existing ones. This allows for more complex transformations to be performed on the data within the DataFrame.
Overall, using chaining with the .assign() method in pandas provides a flexible and efficient way to add new columns to a DataFrame based on the existing data.
How to read CSV files in pandas?
To read a CSV file in pandas, you can use the read_csv() function. Here's an example of how to read a CSV file named "data.csv":
import pandas as pd
Read the CSV file into a DataFrame
df = pd.read_csv("data.csv")
Display the first few rows of the DataFrame
print(df.head())
You can also specify additional parameters when reading the CSV file, such as delimiter, encoding, header, column names, and so on. For example:
# Read a CSV file with a different delimiter and skip rows df = pd.read_csv("data.csv", delimiter=';', skiprows=2)
Read a CSV file with a specific encoding
df = pd.read_csv("data.csv", encoding='utf-8')
Read a CSV file without header and specify column names
df = pd.read_csv("data.csv", header=None, names=['A', 'B', 'C'])
These are just a few examples of how you can read CSV files in pandas. You can refer to the pandas documentation for more information on the read_csv() function and its parameters.
How to group data in pandas based on multiple columns?
You can group data in pandas based on multiple columns by passing a list of column names to the groupby() function. Here is an example:
import pandas as pd
Create a sample DataFrame
data = {'A': [1, 1, 2, 2, 3], 'B': [1, 2, 1, 2, 1], 'C': [10, 20, 30, 40, 50]} df = pd.DataFrame(data)
Group the data based on columns 'A' and 'B'
grouped = df.groupby(['A', 'B'])
Iterate over the groups and print the group keys and data
for group_name, group_data in grouped: print(group_name) print(group_data)
In this example, we are grouping the data based on columns 'A' and 'B'. The resulting output will be the groups based on the unique combinations of values in columns 'A' and 'B'.
How to create a pivot table in pandas?
To create a pivot table in pandas, you can use the pivot_table() method. Here's an example of how to create a pivot table:
import pandas as pd
Create a sample DataFrame
data = { 'Name': ['Alice', 'Bob', 'Charlie', 'Alice', 'Bob', 'Charlie'], 'Category': ['A', 'A', 'B', 'B', 'A', 'B'], 'Value': [10, 20, 30, 40, 50, 60] }
df = pd.DataFrame(data)
Create a pivot table
pivot_table = pd.pivot_table(df, values='Value', index='Name', columns='Category', aggfunc='sum')
print(pivot_table)
This code will create a pivot table where the rows represent the unique values in the 'Name' column, the columns represent the unique values in the 'Category' column, and the values are the sum of the 'Value' column. You can customize the pivot table by changing the values, index, columns, and aggfunc parameters as needed.
How to merge data in pandas?
To merge data in pandas, you can use the merge() function.
Here is an example of how to merge two dataframes in pandas:
import pandas as pd
Create two dataframes
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3'], 'key': ['K0', 'K1', 'K2', 'K3']})
df2 = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'], 'D': ['D0', 'D1', 'D2', 'D3'], 'key': ['K0', 'K1', 'K2', 'K3']})
Merge the two dataframes on the 'key' column
merged_df = pd.merge(df1, df2, on='key')
print(merged_df)
This will merge the two dataframes on the 'key' column, combining the columns from both dataframes where the 'key' values are the same.
How to assign new columns based on chaining in pandas?
To assign new columns based on chaining in pandas, you can use the assign() method. You can chain multiple assign() calls together to create new columns based on existing columns.
Here's an example:
import pandas as pd
Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50], 'C': [100, 200, 300, 400, 500]})
Assign new columns based on chaining
df = df.assign(D = df['A'] + df['B'], E = df['A'] ** 2)
print(df)
This will output:
A B C D E 0 1 10 100 11 1 1 2 20 200 22 4 2 3 30 300 33 9 3 4 40 400 44 16 4 5 50 500 55 25
In this example, we are creating two new columns 'D' and 'E' based on existing columns 'A' and 'B'. We are chaining the assign() method to add multiple new columns in one go.