Pandas Cheat Sheet in Python for Data Science: Complete List for 2025

Widely used in data science, Pandas simplifies handling structured data with its robust functions and intuitive syntax. This Pandas cheat sheet provides a comprehensive overview of commonly used commands and techniques, helping you streamline your workflow and boost efficiency. 

How Will a Pandas Cheat Sheet  Help  You?

A Pandas  cheat sheet is an essential tool for anyone working with data. It bridges the gap between theory and practice by offering quick access to key commands, improving efficiency and accuracy.

Benefits of a Pandas Cheat Sheet:

• Saves time by providing easy access to commonly used commands.
• Keep essential operations like data cleaning and transformation at your fingertips.
• Reduces errors and streamlines processes for complex datasets.
• Highlights key techniques for data manipulation.
• Minimizes syntax mistakes, boosting confidence.

With a cheat sheet, tasks like data cleaning, transformation, and statistics become more manageable, enabling you to tackle real-world challenges effectively.

Also Read: Mastering Pandas: Important Pandas Functions For Your Next Project

Incorporating a Python Pandas cheat sheet into your workflow can significantly enhance your productivity. Let us now have a look at how this sheet can be used.

How to Use this Pandas Cheat Sheet?

The Python Pandas cheat sheet can quickly become an asset for you while handling data analytics band statistics in Python. In this section, let’s cover the major ‘cheats’ that you will frequently use while importing, exporting, cleaning, manipulating, and summarizing data. 

Throughout this cheat sheet, you’ll encounter shorthand abbreviations, including:

• df: DataFrame
• ser: Series
• NaN: Not a Number (missing or undefined data)
• idx: Index
• col: Column
• val: Value
• agg: Aggregate
• str: String
• num: Numeric

These abbreviations make it easier to follow along with the commands and operations.

To make the most of this Python Pandas cheat sheet, it’s important to explore specific operations that you’ll frequently use in data analysis. Let’s break down the core operations you can perform using Pandas with detailed explanations and code examples.

1. Import Data from Different Files

You’ll often need to load data from various sources, and Pandas provides a variety of functions to import data into a DataFrame.

• CSV File:
  The most common file format for datasets is CSV. You can easily load data from a CSV file into a DataFrame using:

import pandas as pd
df = pd.read_csv('file_path.csv')

• Excel File:
  Pandas can also import data from Excel files. Use this command if your data is in .xlsx format:

 df = pd.read_excel('file_path.xlsx', sheet_name='Sheet1')

• SQL Database:
  For data stored in SQL databases, use Pandas to query the database and import data:

 import sqlite3
conn = sqlite3.connect('database.db')
df = pd.read_sql_query('SELECT * FROM table_name', conn)

• JSON File:
  If your data is stored in a JSON file, you can use the following command:

df = pd.read_json('file_path.json')

Also Read: How to Open json File in Excel

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These commands allow you to easily load data from a variety of sources, enabling seamless integration into your Pandas workflow. Let’s now have a look at various export commands.

2. Export DataFrames

After analyzing or manipulating your data, you’ll need to export it back into a file format for sharing or storage. Pandas provides several ways to export your DataFrame.

• Export to CSV:
  You can save your DataFrame as a CSV file using:

 df.to_csv('file_path.csv', index=False)  # Excludes the index column

• Export to Excel:
  To save your DataFrame as an Excel file:

df.to_excel('file_path.xlsx', index=False)

• Export to JSON:
  Save your data in JSON format:

df.to_json('file_path.json')

• Export to SQL:
  If you want to store your data in a SQL database:

 df.to_sql('table_name', conn, if_exists='replace', index=False)

Explanation:

• This command saves your DataFrame (df) as a table in the specified SQL database.
• The exported table will match the structure of your DataFrame, including the column names and data rows.
• The if_exists='replace' parameter ensures that any existing table with the same name is replaced.
• Setting index=False prevents the DataFrame index from being saved as a separate column in the database.

Also Read: Data Frames in Python: Python In-depth Tutorial

These export functions help you store your results in various formats, allowing for further use or sharing with others. 

Now, here are some ways in which you can inspect particular sections of series/DataFrames.

3. How to Inspect a Particular Section of Your DataFrame or Series

Before diving deep into analysis, it’s essential to understand your data’s structure and content. Pandas offers several methods to inspect your DataFrame or Series:

• View the first few rows:
  To quickly inspect the first few rows of your DataFrame:

df.head()  # By default, shows the first 5 rows

View the last few rows:
Similarly, you can view the last few rows:

df.tail()  # By default, shows the last 5 rows

• DataFrame summary:
  To get a concise summary of your DataFrame (e.g., column names, data types, non-null values):

df.info()

What It Shows: The structure of your DataFrame, column data types, and the count of non-null values for each column.

• DataFrame statistics:
  To summarize the numerical data (mean, min, max, etc.):

df.describe()

What It Shows:

• Count: Number of non-null entries for each column.
• Mean, Min, Max: Central tendency and range.
• 25%, 50%, 75%: Quartile values for understanding data distribution.

These commands allow you to quickly inspect and understand the data structure, which is a key step before performing a deeper analysis.

Analyze data like a pro for free with upGrad's free Introduction to Data Analysis using Excel course! Build a strong base to integrate with Python and Pandas.

Now that you know how to inspect your data, let's look at how to select specific parts of it for detailed analysis.

4. How to Select a Specific Subset of Your Data

Selecting a specific subset of data is an important part of data analysis. Here’s how you can select subsets from a DataFrame:

• Select specific columns:
  If you want to select specific columns from your DataFrame:

df[['column1', 'column2']]  # Selects multiple columns

• Select a row by index:
  To access a specific row by its index position:

df.iloc[0]  # Selects the first row by index

• Conditional selection:
  Select rows based on a condition:

df[df['column_name'] > 50]  # Selects rows where column values are greater than 50

Example:

Suppose you have the following DataFrame:

import pandas as pd
data = {'Name': ['Amit', 'Rohit', 'Neha', 'Priya'],
        'Age': [24, 35, 19, 42],
        'Score': [88, 76, 95, 67]}
df = pd.DataFrame(data)
print(df)

Input DataFrame:

  Name  Age  Score
0   Amit   24     88
1  Rohit   35     76
2   Neha   19     95
3  Priya   42     67

Apply Conditional Selection:

# Select rows where Score is greater than 80
filtered_df = df[df['Score'] > 80]
print(filtered_df)

Filtered DataFrame:

  Name  Age  Score
0   Amit   24     88
2   Neha   19     95

• What It Does:
  • Filters rows where the Score column values are greater than 80.
  • Returns only the rows that meet the condition, keeping the same structure as the original DataFrame.

These selection techniques are essential for focusing on relevant data during your analysis.

Also Read: Top 19 Python Tools Every Python Developer Should Know About

After selecting specific subsets, it’s important to clean your data before analysis. Here's how you can do it.

5. Data Cleaning Commands

Data cleaning ensures that your data is in the best shape for analysis. Here are some common commands for cleaning your DataFrame:

• Remove rows with missing values:
  You can drop rows that contain missing values using:

df.dropna(inplace=True)

• Fill missing values:
  If you prefer to fill missing values with a specific value (e.g., 0), use:

df.fillna(0, inplace=True)

• Remove duplicates:
  To remove duplicate rows:

df.drop_duplicates(inplace=True)

Rename columns:
To rename one or more columns in your DataFrame:

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

These data cleaning commands help ensure your data is tidy and ready for further analysis.

Also Read: A Comprehensive Guide to the Data Science Life Cycle: Key Phases, Challenges, and Future Insights

With your data cleaned, it's time to aggregate and organize it using groupby, sorting, and filtering commands.

6. Groupby, Sort, and Filter Data Commands

Grouping, sorting, and filtering your data enables more focused analysis. Below are the most commonly used commands for these operations:

• Group data by a column:
  Use groupby() to group data by a column and apply an aggregation function:

df.groupby('column_name').mean()  # Groups by column and calculates the mean for each group

• Sort data by a column:
  To sort the data in descending order based on a column:

df.sort_values(by='column_name', ascending=False)

• Filter data by condition:
  Filter rows that meet a specific condition:

df[df['column_name'] > 50]

Once your data is grouped, sorted, and filtered, you can enhance your workflow by chaining methods together.

Example:
Suppose you have a sales dataset:

import pandas as pd
data = {'Region': ['North', 'South', 'North', 'East', 'South'],
        'Sales': [200, 150, 300, 400, 250],
        'Profit': [50, 30, 70, 100, 60]}
df = pd.DataFrame(data)

Input DataFrame:

 Region  Sales  Profit
0  North    200      50
1  South    150      30
2  North    300      70
3   East    400     100
4  South    250      60

Group by Region and calculate mean sales and profit:

grouped_df = df.groupby('Region').mean()
print(grouped_df)

Output:

    Sales  Profit
Region                
East     400.0   100.0

North    250.0    60.0
South    200.0    45.0

Use Case: Analyze average sales and profit per region to identify high-performing areas.

7. Method Chaining

Method chaining allows you to apply multiple operations in one line, improving the readability and efficiency of your code.

Example:

df.dropna().sort_values(by='column_name').reset_index(drop=True)

This example demonstrates how to drop missing values, sort the data, and reset the index, all in a single line.

After chaining methods together, summarizing your data will give you a high-level view of its key statistics.

8. Summarize Data

Summarizing your data helps you quickly understand its key features. Use the following commands to get a summary of your data:

• Mean, Median, and Mode:
  To calculate the mean, median, or mode of a column:

df['column_name'].mean()  # Mean
df['column_name'].median()  # Median
df['column_name'].mode()  # Mode

• Use describe():
  To get a quick summary of numerical columns:

df.describe()  # Provides a summary for numerical columns

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Once you’ve summarized your data, reshaping it will allow for better organization and easier analysis.

9. Reshaping Data

Reshaping your data can help prepare it for analysis or visualization. Pandas provides several methods for reshaping:

• Melt the DataFrame:
  This command transforms the DataFrame into a long format:

pd.melt(df, id_vars=['column1'], value_vars=['column2', 'column3'])

• Pivot the DataFrame:
  Pivot the DataFrame to restructure it:

df.pivot(index='column1', columns='column2', values='column3')

After reshaping your data, you can handle missing values to ensure completeness before proceeding further.

10. Handling Missing Data

Handling missing data is an essential part of data cleaning. Here are some common methods:

• Drop rows with missing values:

df.dropna()

• Fill missing values:

df.fillna(value=0)

Now that your data is complete, it's time to create new columns that will help with further analysis.

11. Making New Columns

Creating new columns based on existing data can enhance your analysis:

• Create a new column:

df['new_column'] = df['column1'] + df['column2']

Once new columns are made, visualizing your data through plotting can help identify patterns and insights.

12. Plotting

Pandas make it easy to visualize your data:

• Plot a line chart:

df['column_name'].plot(kind='line')

• Plot a histogram:

df['column_name'].plot(kind='hist')

With your data visualized, combining different datasets can provide a broader perspective for analysis.

13. Combining Data Sets

Combine multiple DataFrames for more comprehensive analysis:

• Concatenate DataFrames:

pd.concat([df1, df2], axis=0)

• Merge DataFrames:

pd.merge(df1, df2, on='column_name')

Finally, you can refine your queries and analyze subsets of data more efficiently using the query method.

14. Using Query

The query() method allows you to select rows based on a condition:

• Using query for filtering:

df.query('column_name > 50')

This Pandas cheat sheet equips you with the key commands and functions to handle data efficiently, from importing and cleaning to analyzing and visualizing. 

Example:
Filter rows where Score > 80 and Age < 30:

complex_filter = df.query('Score > 80 and Age < 30')
print(complex_filter)

Output:

 Name  Age  Score
0   Amit   24     88
2   Neha   19     95

Use Case: Identify younger individuals with high scores for targeted interventions.

Advantages of query()

Readability: Offers a more intuitive syntax compared to traditional filtering methods.

# Traditional
df[(df['Score'] > 80) & (df['Age'] < 30)]
# Using query
df.query('Score > 80 and Age < 30')

Performance: Slightly faster for large datasets as it avoids intermediate DataFrame creation.

Also Read: Data Analysis Using Python [Everything You Need to Know]

Pandas also provide commands for advanced data manipulation which will be discussed next.

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Pandas Commands Cheat Sheet for Advanced Data Manipulation

Advanced data manipulation is essential when working with complex datasets. In this section of the Python Pandas cheat sheet, you'll learn commands for combining and merging DataFrames, working with dates and times, and handling categorical data. 

These commands help you refine and manipulate your data for more sophisticated analysis, improving your ability to extract meaningful insights from raw data. 

Let’s dive into the most important Pandas commands for advanced data manipulation.

Combining & Merging DataFrames

Combining and merging DataFrames is a common task when dealing with datasets spread across multiple tables or files. Use the following commands to combine and merge data efficiently:

• Concatenate along rows (axis=0):

pd.concat([df1, df2], axis=0)

This command stacks the DataFrames vertically (rows). If the columns in the DataFrames match, they will align automatically.

• Concatenate along columns (axis=1):

pd.concat([df1, df2], axis=1)

This command merges the DataFrames horizontally by aligning their indexes.

• Merge on a specific column:

pd.merge(df1, df2, on='column_name')

This command merges two DataFrames based on a common column. The on parameter specifies the column to join on.

• Merge with different keys:

pd.merge(df1, df2, left_on='column1', right_on='column2')

When the columns you want to merge on have different names in the two DataFrames, you can specify which column from each DataFrame to join using left_on and right_on.

• Outer, Inner, Left, and Right Joins:

pd.merge(df1, df2, how='inner', on='column_name')  # Default: Inner Join
pd.merge(df1, df2, how='outer', on='column_name')  # Outer Join
pd.merge(df1, df2, how='left', on='column_name')   # Left Join
pd.merge(df1, df2, how='right', on='column_name')  # Right Join

• Multi-Indexing:
  Pandas supports combining DataFrames with hierarchical indexing. This is useful for datasets with multi-level groupings, such as year-wise and region-wise sales data.

df.set_index(['Year', 'Region']).join(other_df.set_index(['Year', 'Region']))

• Custom Functions with apply:
  Enhance merging by applying custom logic to merged columns.

df['Discounted_Price'] = df.apply(lambda row: row['Price'] * 0.9 if row['Category'] == 'Sale' else row['Price'], axis=1)

Example: Add a calculated column for discounted prices after merging product details with sales.

• Performance Optimization with Dask:
  For large datasets that exceed memory limits, use Dask, which extends Pandas' functionality for parallel processing.

import dask.dataframe as dd
ddf1 = dd.from_pandas(df1, npartitions=4)
ddf2 = dd.from_pandas(df2, npartitions=4)
merged_ddf = dd.merge(ddf1, ddf2, on='column_name'

Example: Merge customer and transaction data for millions of records efficiently.

These commands specify how rows from two DataFrames are combined based on matching keys.

Now that you know how to bring your data together, let's explore another vital aspect—managing time-related information.

Working with Dates and Times

Handling date and time data is a crucial aspect of data analysis, especially when dealing with time series data or logs. Below are key commands for working with dates and times in Pandas:

• Convert a column to datetime:

df['date_column'] = pd.to_datetime(df['date_column'])

• Specify date format during conversion:

df['date_column'] = pd.to_datetime(df['date_column'], format='%Y-%m-%d')

This ensures your date column is correctly recognized as a datetime object for manipulation.

Extract year, month, and day:

df['year'] = df['date_column'].dt.year
df['month'] = df['date_column'].dt.month
df['day'] = df['date_column'].dt.day

• Extract weekday:

df['weekday'] = df['date_column'].dt.weekday

• Extract time from datetime:

df['hour'] = df['date_column'].dt.hour
df['minute'] = df['date_column'].dt.minute

• Add/Subtract time:

df['new_column'] = df['date_column'] + pd.DateOffset(days=5)
df['new_column'] = df['date_column'] - pd.DateOffset(weeks=2)

• Calculate time difference:

df['time_diff'] = df['date_column'] - df['other_date_column']

These commands help you manipulate and analyze datetime data efficiently.

Once you master time management, you’ll want to refine your categories for better data representation.

Handling Categorical Data

Categorical data, such as labels or categories, can be tricky to work with, but Pandas provides tools to handle them efficiently. Below are some commands to work with categorical variables.

Convert a column to a categorical type:

df['category_column'] = df['category_column'].astype('category')

• Convert string column to categories:

df['category_column'] = pd.Categorical(df['category_column'])

• Get the categories of a column:

df['category_column'].cat.categories

• Rename categories:

df['category_column'].cat.rename_categories(['new_cat1', 'new_cat2'], inplace=True)

• Assign categories to a new column:

df['new_category_column'] = df['category_column'].cat.codes

• Check if a column is categorical:

df['category_column'].dtype

• Sort categorical values:

df['category_column'] = df['category_column'].cat.reorder_categories(['new_cat2', 'new_cat1'], ordered=True)

Also Read: 4 Types of Data: Nominal, Ordinal, Discrete, Continuous

When working with complex datasets, a handy cheat sheet can save you time and effort. Once you’re familiar with the commands, it's time to focus on efficiency.

Tips for Efficient Data Analysis While Using Python Pandas Cheat Sheet

While the Pandas cheat sheet is an excellent reference tool, there are several tips and best practices that can help you make the most of it. These tips will not only boost your efficiency but also optimize your data analysis workflows. 

Let's take a look at some strategies you can implement while using the Python Pandas cheat sheet to streamline your tasks.

Optimise Memory Usage

Managing memory effectively is crucial when working with large datasets. Pandas can consume significant memory, especially when working with big DataFrames. 

Here's how you can optimize memory usage:

• Use appropriate data types:
  Instead of using the default float64 or int64, convert columns to more memory-efficient types like int8, int16, or category for categorical data.

df['column_name'] = df['column_name'].astype('category')

• Read data in chunks:
  If you are working with large files, use the chunksize parameter while reading data to load it in smaller pieces.

chunk = pd.read_csv('large_file.csv', chunksize=10000)

• Drop unnecessary columns:
  Eliminate any columns you don’t need before performing analysis, freeing up memory.

df.drop(columns=['unnecessary_column'], inplace=True)

Optimizing memory usage helps prevent your system from becoming slow or running out of memory when handling large datasets.

Now, here is a quick look at using vectorized operations.

Use Vectorized Operations

Pandas is built to support vectorized operations, which allow you to apply functions to entire columns or rows at once. This is much faster than iterating over individual elements. 

Here's how you can take advantage of vectorized operations:

• Perform calculations on entire columns:
  Instead of using loops, apply operations directly to the entire DataFrame or Series.

df['new_column'] = df['column1'] + df['column2']

• Avoid apply() when possible:
  While apply() can be useful, it is slower than built-in vectorized operations. Try to use Pandas functions whenever possible.

# Slow:
df['new_column'] = df['column1'].apply(lambda x: x*2)
# Faster:
df['new_column'] = df['column1'] * 2

Vectorized operations simplify repetitive tasks and enhance performance. With this powerful tool, the next step is knowing when and how to consult the documentation for deeper insights.

Consult Documentation

Although the Python Pandas cheat sheet provides a quick reference for Pandas commands, the official Pandas documentation is an invaluable resource for deeper understanding. Here’s how you can make the most of it:

• Look up functions and methods:
  The Pandas documentation provides detailed descriptions, examples, and additional options for each function. If you need more context on a command, visit the official site.
• Explore advanced functionality:
  The documentation also covers advanced features like multi-indexing, complex merging, and optimization techniques that may not be listed on the Pandas cheat sheet.
• Stay up to date:
  Pandas is regularly updated with new features and improvements. By consulting the documentation, you can keep track of the latest functionalities.

The Pandas cheat sheet is perfect for quick reference, but consulting the official documentation will give you a deeper understanding and help you handle more advanced data manipulation tasks.

Also Read: Data Preprocessing in Machine Learning: 7 Key Steps to Follow, Strategies, & Applications

These tips can streamline your data analytics process, but challenges may still arise when working with diverse datasets. To help you navigate them effectively, here’s a quick look at common problems and their solutions.

Common Challenges While Using Pandas Cheat Sheet and How to Overcome Them

Using a Pandas cheat sheet can present challenges like information overload and syntax confusion. Here's how to overcome them.

Overwhelming Amount of Information

One of the challenges you might face when using a Pandas cheat sheet is the sheer amount of information it contains. The cheat sheet covers many Pandas commands, which can be overwhelming, especially for beginners. 

With so many commands and functions to choose from, it's easy to feel lost. Here’s the solution for this issue:

Solution:

• Break down the cheat sheet into sections:
  Focus on the sections that are most relevant to your current task. For example, if you're working on cleaning data, concentrate on the data-cleaning commands.
• Start with the basics:
  Begin with essential commands like reading data, basic filtering, and selecting columns. Once you're comfortable, gradually explore more advanced operations.
• Use the cheat sheet as a quick reference:
  Don’t feel the need to memorize everything. Instead, use it to quickly find the command you need and apply it as you go.

Once you manage the information overload, the next challenge is understanding contextual examples.

Difficulty in Contextual Examples

Another challenge when using the Pandas cheat sheet is understanding how the commands apply to your specific use case. The cheat sheet provides a list of Pandas commands, but it doesn’t always offer contextual examples or detailed explanations of when and why to use them.

A simple solution for this is as follows:

Solution:

• Consult external resources for examples:
  If a command isn’t clear, refer to Pandas documentation or online tutorials that provide contextual use cases. Look for examples of the command being applied to real-world datasets.
• Experiment with the commands:
  Create small sample DataFrames and experiment with the commands to see their behavior. This hands-on practice will help you understand how to apply the commands to your own data.
• Use online communities:
  Platforms like Reddit can help you understand how to apply specific Pandas commands in your projects. Engaging with others can provide practical examples and solutions to common challenges.

Grasping examples in the right context can be tricky when applying new concepts. As you work through examples, you might also face hurdles in mastering the correct syntax. Here’s how to deal with this issue.

Syntax Confusion

Even though the Pandas cheat sheet provides a quick overview of Pandas commands, the syntax for some functions can still be confusing, especially for more complex operations. This can lead to errors and delays when you’re working with data.

Here’s how to deal with this:

Solution:

• Break down complex syntax:
  Take a step-by-step approach to complex commands. Write out each part of the syntax and understand what it does before combining it into a full command.
• Practice with simple examples first:
  Start with simple Pandas commands and gradually move to more complex ones. Once you're familiar with basic syntax, transitioning to more advanced commands will feel more intuitive.
• Use online resources for clarification:
  If you're stuck on a syntax issue, refer to Pandas documentation or search for examples in online tutorials. Many resources provide explanations for tricky commands and common mistakes.

Syntax errors are common when you're just starting out or transitioning to advanced concepts. After gaining clarity on syntax, the next step is addressing compatibility issues like version mismatches.

Version Mismatches

One challenge that users may face is dealing with version mismatches. Since Pandas is actively developed and frequently updated, commands or functions may behave differently across versions. This can cause compatibility issues when using the Pandas cheat sheet with older or newer versions of Pandas.

Some solutions for this problem include:

Solution:

• Check your Pandas version:
  You can easily check your Pandas version using the following command:

import pandas as pd
print(pd.__version__)

• Upgrade or downgrade Pandas:
  If your version doesn’t support certain features, you can either upgrade to the latest version or install an older version to match the Pandas cheat sheet:

pip install --upgrade pandas  # To upgrade
pip install pandas==<version_number>  # To install a specific version

• Consult version-specific documentation:
  Pandas documentation provides information about changes in each version, helping you understand which commands are supported in your version. You can also find backward-compatible commands if you're using an older version.

Along with challenges, understanding common mistakes can help you avoid inefficiencies and optimize your use of Pandas.

Common Mistakes to Avoid While Using Pandas

When working with Pandas, being aware of potential pitfalls can save time and prevent inefficiencies. Here are some common mistakes to watch out for, along with tips to avoid them:

1. Overusing apply Instead of Vectorized Operations

• Mistake: Using .apply() for operations that could be handled by Pandas' built-in vectorized functions.
• Why It’s a Problem: .apply() is slower and less efficient, especially on large datasets.

Example:

# Inefficient
df['new_col'] = df['col'].apply(lambda x: x * 2)

# Efficient
df['new_col'] = df['col'] * 2

• Tip: Always use vectorized operations when possible; they are optimized for performance.

2. Ignoring Memory Usage for Large Datasets

• Mistake: Loading large datasets without considering memory constraints.
• Why It’s a Problem: Pandas keeps all data in memory, which can lead to crashes with very large files.
• Solution: Use dtypes to optimize memory usage:

df = pd.read_csv('data.csv', dtype={'col1': 'int32', 'col2': 'float32'})

#Use chunksize for large CSVs:

for chunk in pd.read_csv('large_data.csv', chunksize=10000):
 process(chunk)

For even larger datasets, consider alternatives like Dask or PySpark.

3. Forgetting to Reset the Index

• Mistake: Performing operations like filtering or merging without resetting the index.
• Why It’s a Problem: Can lead to misaligned data or unexpected results.
• Solution: After filtering or modifying a DataFrame, reset the index to ensure proper alignment:

df = df.reset_index(drop=True)

4. Using append() in Loops

• Mistake: Adding rows to a DataFrame inside a loop using .append().
• Why It’s a Problem: .append() creates a new DataFrame each time, making it very slow for large datasets.
• Solution: Use a list to collect rows and create a DataFrame at the end:

rows = []
for i in range(1000):
    rows.append({'col1': i, 'col2': i * 2})
df = pd.DataFrame(rows)

5. Failing to Handle Missing Data Early

• Mistake: Delaying the handling of missing values, leading to errors in subsequent calculations or visualizations.
• Why It’s a Problem: Missing data can distort results or break certain functions.

Solution: Address missing values upfront:

df['col'].fillna(0, inplace=True)  # Replace missing values with 0
df.dropna(subset=['important_col'], inplace=True)  # Drop rows with missing values in specific columns

6. Using Inefficient Filtering

• Mistake: Writing overly complex or redundant filtering logic.
• Why It’s a Problem: Increases code complexity and execution time.
• Solution: Use concise and optimized filtering:

# Inefficient
df_filtered = df[(df['col1'] > 50) & (df['col1'] < 100)]

# Efficient
df_filtered = df.query('50 < col1 < 100')

7. Not Leveraging read_* and to_* Functions Efficiently

• Mistake: Ignoring available options in read_csv, read_excel, and other file input/output functions.
• Why It’s a Problem: Leads to unnecessary manual pre-processing.
• Solution: Use parameters like usecols, nrows, and skiprows to load only relevant data:

df = pd.read_csv('data.csv', usecols=['col1', 'col2'], nrows=100)

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Reference Link:

https://pandas.pydata.org/pandas-docs/stable/