Pandas Cheatsheet

The Pandas cheatsheet provides a fundamental reference to all the core concepts of pandas. This powerful library in Python is used for data manipulation, analysis, and handling structured data with ease. Whether you're working with large datasets, performing data cleaning, or analyzing trends, this cheat sheet will help you navigate Pandas easily. Go through the cheatsheet and learn the Python pandas library.

• Introduction to Pandas
• Installing Pandas
• Creating DataFrames
• Creating Series
• Reading Data
• Writing Data
• Selecting Columns
• Selecting Rows
• Filtering Data
• Boolean Indexing
• Querying Data
• Handling Missing Values
• Changing Data Types
• Renaming Columns
• Duplicates
• Replacing Values
• Sorting Data
• GroupBy
• Pivot Tables
• Apply Functions
• Merging and Joining
• Summary Statistics
• Value Counts
• Correlation
• Cumulative Functions
• MultiIndex
• Time Series Analysis
• Working with JSON
• Visualization

1. Introduction to Pandas

In the introduction, Pandas is a popular open-source library in Python for data analysis. It provides data structures and functions to processes large datasets which includes tabular data such as spreadsheets and SQL tables. Here, we will learn how to import the pandas library.

 import pandas as pd 

2. Installing Pandas

To install Pandas on the system, use the following command −

 pip install pandas 

3. Creating DataFrames

The dataframe can be created using lists, dictionaries, and external data sources.

 # Creating a DataFrame from a dictionary import pandas as pd inp_data = {"Name": ["Ravi", "Faran"], "Age": [25, 30]} df = pd.DataFrame(inp_data) print(df) 

4. Creating Series

In Pandas, the series is like a column in the table. You can create the pandas series using a list or NumPy arrays.

 import pandas as pd s = pd.Series([10, 20, 30, 40]) 

5. Reading Data

There are four methods to read data in Pandas − CSV, Excel, JSON, and SQL files.

 # Reading a CSV file df = pd.read_csv("data.csv") 

6. Writing Data

To write the pandas dataframe in a CSV file, the user needs Dataframe.to_csv().

 # Writing a DataFrame to a CSV file df.to_csv("output.csv", index=False) 

7. Selecting Columns

To select the specific column from dataframe −

 # Selecting a single column df["Name"] 

8. Selecting Rows

To retrieve specific rows using index selection and slicing, Pandas provides the head() and tail() methods. The head() method returns the first few rows of the DataFrame, while the tail() method retrieves the last few rows.

 df.head(5) 

Or,

 df.tail(5) 

9. Filtering Data

Filtering data in pandas means it applies some conditions based on certain rows and columns.

 # Filtering rows where Age > 25 df[df["Age"] > 25] 

10. Boolean Indexing

In pandas, boolean indexing means the process of filtering data using a boolean array.

 mask = df["Age"] > 25 df[mask] 

11. Querying Data

In Pandas, querying data filters the dataframe by passing the condition as a string that returns matching rows. You can use the query() method.

 df.query("Age > 25") 

12. Handling Missing Values

To handle the missing values in Pandas, use the methods like dropna() and fillna(). Below is the implementation −

 df.fillna(0, inplace=True) 

Or,

 import pandas as pd # Creating a DataFrame with missing values data = {"Name": ["Vivek", "Faran", None, "Revathi"], "Age": [25, None, 30, 35]} df = pd.DataFrame(data) # Dropping rows with missing values df_result = df.dropna() print(df_result) 

13. Changing Data Types

To convert the data types in Python use the method astype(). This ensures the proper formatting.

 df["Age"] = df["Age"].astype(int) 

14. Renaming Columns

The easier way to rename the columns in Pandas, use the method rename(). The following syntax is given below −

 df.rename(columns={"old_name": "new_name"}, inplace=True) 

15. Duplicates

To remove the duplicates from the rows, use the method drop_duplicates().

 df.drop_duplicates(inplace=True) 

16. Replacing Values

The term "replacing" is also known as "removing". To remove the specific values in a dataframe, use the method replace().

 df["column_name"].replace({"old_value": "new_value"}, inplace=True) 

17. Sorting Data

In Python, Pandas is a popular library that provides a built-in method called sort_values(). This method allows users to sort the values of a DataFrame or Series in ascending or descending order.

 import pandas as pd data = {'Name': ['Alex', 'John', 'Sunny', 'Usha'], 'Id': [2115, 6330, 8135, 4110], 'Score': [85, 90, 95, 80]} df = pd.DataFrame(data) # Sorting by 'Id' in ascending order sorted_df = df.sort_values(by='Id') print(sorted_df) 

18. GroupBy

GroupBy is used to split the data into groups based on some criteria and then apply a function to each group. Thus, this helps in data summarization and analyzing.

 # Grouping by 'Gender' and calculating the mean age df.groupby('Gender')['Age'].mean() 

19. Pivot Tables

In Pandas, the use of pivot tables is to summarize the data that allows users to aggregate data across multiple dimensions.

 df.pivot_table(values='Age', index='Gender', columns='City', aggfunc='mean') 

20. Apply Functions

In pandas, the apply() function is used to apply a function along the axis of a DataFrame or Series.

 df.apply(lambda x: x.max() - x.min()) 

21. Merging and Joining

In Pandas, the concept of merging and joining allows users to combine multiple dataframes based on shared columns or indexes.

 # Merging two DataFrames on a common column 'ID' df1.merge(df2, on='ID') 

Or,

 df1.join(df2, on='column_name', how='inner') 

Explanation of join() parameters −

• on: Specifies the column or index to join on.
• how: This determine the type of join used for the dataset.

22. Summary Statistics

Summary statistics help in understanding the distribution and key properties of the dataset. Methods like 'mean()', 'median()', and 'std()' provide insights of the data from the given datasets.

 # Getting summary statistics df.describe() 

23. Value Counts

The value_counts() method is used to get the frequency of unique values in a column.

 df['col_name'].value_counts() 

24. Correlation

Correlation means the relationship between two variables. The corr() method calculates the correlation coefficient between columns in a DataFrame.

 df.corr() 

25. Cumulative Functions

In Pandas, cumulative functions are those functions that add up or multiply values sequentially over time. You can use methods like cumsum() and cumprod().

 df['Age'].cumsum() 

26. MultiIndex

The MultiIndex is a very simple concept that adds multiple levels of indexing in a DataFrame. So, it is possible to handle the complex data structure.

 arrays = [['A', 'A', 'B', 'B'], [1, 2, 1, 2]] index = pd.MultiIndex.from_arrays(arrays, names=('Letter', 'Number')) df_multi = pd.DataFrame({'Value': [10, 20, 30, 40]}, index=index) 

27. Time Series Analysis

Time series analysis works with time-indexed data. Pandas provides functionality to handle time series data by containing date parsing and resampling.

 # Converting a column to datetime format df['Date'] = pd.to_datetime(df['Date']) # Resampling data by month df.resample('M').mean() 

28. Working with JSON

JSON (JavaScript Object Notation) is a popular data format. In pandas, we have two ways to implement JSON −

• read_json() − It read the JSON data into a dataframe.
• to_json() − It convert dataframes into JSON format.

 # read JSON data into a DataFrame df = pd.read_json('data.json') # convert DataFrame into JSON df.to_json('output.json') 

29. Visualization

Data visualization is key to understanding patterns and insights. Pandas integrates with libraries like Matplotlib and Seaborn to create various plots from DataFrames.

 # Plotting a line graph using Pandas df['Age'].plot(kind='line') # Plotting a histogram df['Age'].plot(kind='hist', bins=10)