Pandas Cheat Sheet — Python for Data Science (2023)

March 4, 2020

Pandas Cheat Sheet — Python for Data Science (1)

If you\’re interested in working with data in Python, you\’re almost certainly going to be using the pandas library. But even when you\’ve learned pandas — perhaps in our interactive pandas course — it\’s easy to forget the specific syntax for doing something. That\’s why we\’ve created a pandas cheat sheet to help you easily reference the most common pandas tasks.

Before we dive into the cheat sheet, it\’s worth mentioning that you shouldn\’t rely on just this. If you haven\’t learned any pandas yet, we\’d strongly recommend working through our pandas course. This cheat sheet will help you quickly find and recall things you\’ve already learned about pandas; it isn\’t designed to teach you pandas from scratch!

It\’s also a good idea to check to the official pandas documentation from time to time, even if you can find what you need in the cheat sheet. Reading documentation is a skill every data professional needs, and the documentation goes into a lot more detail than we can fit in a single sheet anyway!

If you\’re looking to use pandas for a specific task, we also recommend checking out the full list of our free Python tutorials; many of them make use of pandas in addition to other Python libraries. In our Python datetime tutorial, for example, you\’ll also learn how to work with dates and times in pandas.

Pandas Cheat Sheet: Guide

First, it may be a good idea to bookmark this page, which will be easy to search with Ctrl+F when you\’re looking for something specific. However, we\’ve also created a PDF version of this cheat sheet that you can download from here in case you\’d like to print it out.

In this cheat sheet, we\’ll use the following shorthand:

df | Any pandas DataFrame object s | Any pandas Series object

As you scroll down, you\’ll see we\’ve organized related commands using subheadings so that you can quickly search for and find the correct syntax based on the task you\’re trying to complete.

(Video) Data Analysis with Python - Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn)

Also, a quick reminder — to make use of the commands listed below, you\’ll need to first import the relevant libraries like so:

import pandas as pdimport numpy as np

Importing Data

Use these commands to import data from a variety of different sources and formats.

pd.read_csv(filename) | From a CSV file pd.read_table(filename) | From a delimited text file (like TSV) pd.read_excel(filename) | From an Excel file pd.read_sql(query, connection_object) | Read from a SQL table/database pd.read_json(json_string) | Read from a JSON formatted string, URL or file. pd.read_html(url) | Parses an html URL, string or file and extracts tables to a list of dataframes pd.read_clipboard() | Takes the contents of your clipboard and passes it to read_table() pd.DataFrame(dict) | From a dict, keys for columns names, values for data as lists ## Exporting Data

Use these commands to export a DataFrame to CSV, .xlsx, SQL, or JSON.

df.to_csv(filename) | Write to a CSV file df.to_excel(filename) | Write to an Excel file df.to_sql(table_name, connection_object) | Write to a SQL table df.to_json(filename) | Write to a file in JSON format ## Create Test Objects

These commands can be useful for creating test segments.

pd.DataFrame(np.random.rand(20,5)) | 5 columns and 20 rows of random floats pd.Series(my_list) | Create a series from an iterable my_list df.index = pd.date_range('1900/1/30', periods=df.shape[0]) | Add a date index ## Viewing/Inspecting Data

Use these commands to take a look at specific sections of your pandas DataFrame or Series.

df.head(n) | First n rows of the DataFrame df.tail(n) | Last n rows of the DataFrame df.shape | Number of rows and columns df.info() | Index, Datatype and Memory information df.describe() | Summary statistics for numerical columns s.value_counts(dropna=False) | View unique values and counts df.apply(pd.Series.value_counts) | Unique values and counts for all columns ## Selection

(Video) Python for Data Science - Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib)

Use these commands to select a specific subset of your data.

df[col] | Returns column with label col as Series df[[col1, col2]] | Returns columns as a new DataFrame s.iloc[0] | Selection by position s.loc['index_one'] | Selection by index df.iloc[0,:] | First row df.iloc[0,0] | First element of first column ## Data Cleaning

Use these commands to perform a variety of data cleaning tasks.

df.columns = ['a','b','c'] | Rename columns pd.isnull() | Checks for null Values, Returns Boolean Arrray pd.notnull() | Opposite of pd.isnull() df.dropna() | Drop all rows that contain null values df.dropna(axis=1) | Drop all columns that contain null values df.dropna(axis=1,thresh=n) | Drop all rows have have less than n non null values df.fillna(x) | Replace all null values with x s.fillna(s.mean()) | Replace all null values with the mean (mean can be replaced with almost any function from the statistics module) s.astype(float) | Convert the datatype of the series to float s.replace(1,'one') | Replace all values equal to 1 with \’one\’ s.replace([1,3],['one','three']) | Replace all 1 with \’one\’ and 3 with \’three\’ df.rename(columns=lambda x: x + 1) | Mass renaming of columns df.rename(columns={'old_name': 'new_ name'}) | Selective renaming df.set_index('column_one') | Change the index df.rename(index=lambda x: x + 1) | Mass renaming of index ## Filter, Sort, and Groupby

Use these commands to filter, sort, and group your data.

df[df[col] > 0.5] | Rows where the column col is greater than 0.5df[(df[col] > 0.5) & (df[col] < 0.7)] | Rows where 0.7 > col > 0.5df.sort_values(col1) | Sort values by col1 in ascending order df.sort_values(col2,ascending=False) | Sort values by col2 in descending order df.sort_values([col1,col2],ascending=[True,False]) | Sort values by col1 in ascending order then col2 in descending order df.groupby(col) | Returns a groupby object for values from one column df.groupby([col1,col2]) | Returns groupby object for values from multiple columns df.groupby(col1)[col2] | Returns the mean of the values in col2, grouped by the values in col1 (mean can be replaced with almost any function from the statistics module) df.pivot_table(index=col1,values=[col2,col3],aggfunc=mean) | Create a pivot table that groups by col1 and calculates the mean of col2 and col3df.groupby(col1).agg(np.mean) | Find the average across all columns for every unique col1 group df.apply(np.mean) | Apply the function np.mean() across each column nf.apply(np.max,axis=1) | Apply the function np.max() across each row ## Join/Combine

Use these commands to combine multiple dataframes into a single one.

df1.append(df2) | Add the rows in df1 to the end of df2 (columns should be identical) pd.concat([df1, df2],axis=1) | Add the columns in df1 to the end of df2 (rows should be identical) df1.join(df2,on=col1,how='inner') | SQL-style join the columns in df1 with the columns on df2 where the rows for col have identical values. 'how' can be one of 'left', 'right', 'outer', 'inner'## Statistics

Use these commands to perform various statistical tests. (These can all be applied to a series as well.)

(Video) 22. Pandas Cheat Sheets | Data Collection & Wrangling Course By Sana Rasheed

df.describe() | Summary statistics for numerical columns df.mean() | Returns the mean of all columns df.corr() | Returns the correlation between columns in a DataFrame df.count() | Returns the number of non-null values in each DataFrame column df.max() | Returns the highest value in each column df.min() | Returns the lowest value in each column df.median() | Returns the median of each column df.std() | Returns the standard deviation of each column ## Download a printable version of this cheat sheet

If you’d like to download a printable version of this cheat sheet you can do so here.

Further Resources

If you’d like to learn more about this topic, check out Dataquest\’s interactive Pandas and NumPy Fundamentals course, and our Data Analyst in Python, and Data Scientist in Python paths that will help you become job-ready in around 6 months.

Learn PythonPandasResources

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FAQs

Is pandas enough for data science? ›

Pandas is an open-source Python Library useful for performing various data manipulation and data analysis operations in the field of Data Science. It was released in 2009 and has become a popular tool for performing data analysis operations.

What is pandas cheat sheet? ›

The Pandas cheat sheet will guide you through the basics of the Pandas library, going from the data structures to I/O, selection, dropping indices or columns, sorting and ranking, retrieving basic information of the data structures you're working with to applying functions and data alignment.

How long it takes to master pandas? ›

How Long Does it Take to Learn Pandas? If you already know Python, you will need about two weeks to learn Pandas. Without a background in Python, you'll need one to two months to learn Pandas.

Are pandas hard Python? ›

pandas is one of the first Python packages you should learn because it's easy to use, open source, and will allow you to work with large quantities of data. It allows fast and efficient data manipulation, data aggregation and pivoting, flexible time series functionality, and more.

Can Python handle 1 billion rows? ›

Introduction to Vaex. Vaex is a python library that is an out-of-core dataframe, which can handle up to 1 billion rows per second. 1 billion rows.

Is SQL harder than pandas? ›

In Pandas, it is easy to get a quick sense of the data; in SQL it is much harder. Pandas has native support for visualization; SQL does not. Pandas makes it easy to do machine learning; SQL does not.

Is pandas better than Excel? ›

Speed - Pandas is much faster than Excel, which is especially noticeable when working with larger quantities of data. Automation - A lot of the tasks that can be achieved with Pandas are extremely easy to automate, reducing the amount of tedious and repetitive tasks that need to be performed daily.

Is there a Python cheat sheet? ›

Cheatography is a two-page Python cheat sheet for quick reference. It covers Python sys variables, sys. argv, special methods, file methods, list methods, string methods, Python os variables, DateTime methods, and Python indexes and slices.

Can I use pandas instead of Excel? ›

Most of the tasks you can do in Excel can be done in Pandas too and vice versa. That said, there are many areas where Pandas outperforms Excel. In this introduction to Pandas, we will compare Pandas dataframes and Excel Spreadsheet, learn different ways to create a dataframe, and how to make pivot tables.

Can I learn pandas in one day? ›

Learning Numpy or Pandas will take around 1 week.

Is Python enough to get a job? ›

Python is used in many different areas. You can search for a job as a Python developer, data scientist, machine learning specialist, data engineer, and more. These jobs are interesting and in-demand. And, like other Python jobs, they pay good salaries.

Why are pandas difficult? ›

Pandas is Powerful but Difficult to use

While it does offer quite a lot of functionality, it is also regarded as a fairly difficult library to learn well. Some reasons for this include: There are often multiple ways to complete common tasks. There are over 240 DataFrame attributes and methods.

How can I learn Panda fast? ›

How to Learn Pandas: Step-by-Step
  1. Decide why you want to learn Pandas. ...
  2. Know Python. ...
  3. Get familiar with the functionalities of Pandas. ...
  4. Install Pandas. ...
  5. Start with basic Excel/Pandas projects. ...
  6. As your skills grow, try more advanced projects. ...
  7. Keep learning and join the community.
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Is pandas better than SQL? ›

As we've seen in the previous post on Pandas vs. SQL, Pandas has over 600+ functions that let you operate on data in a variety of powerful ways that are either impossible or extremely hard to do in SQL, spanning a range of key machine learning, linear algebra, featurization, and data cleaning operations.

Is Python harder than Java? ›

Java and Python are two of the most popular programming languages. Of the two, Java is the faster language, but Python is simpler and easier to learn. Each is well-established, platform-independent, and part of a large, supportive community.

How big is too big for a Python list? ›

According to the source code, the maximum size of a list is PY_SSIZE_T_MAX/sizeof(PyObject*) . On a regular 32bit system, this is (4294967295 / 2) / 4 or 536870912. Therefore the maximum size of a python list on a 32 bit system is 536,870,912 elements.

What is the biggest number in Python? ›

value, which corresponds to 18,446,744,073,709,551,615 for the unsigned data type and ranges from -9,223,372,036,854,775,807 to 9,223,372,036,854,775,807 in the signed version.

Is Pandas good for big data? ›

Pandas uses in-memory computation which makes it ideal for small to medium sized datasets. However, Pandas ability to process big datasets is limited due to out-of-memory errors. A number of alternatives to Pandas are available, one of which is Apache Spark.

Do Data engineers need Pandas? ›

Pandas is a powerful tool for cleaning, transforming, manipulating, or enriching data, among many other potential uses. As a result it has become a standard tool for data engineers for a wide range of applications.

Which is better for data science SQL or Python? ›

If someone is really looking to start their career as a developer then they should start with SQL because it's a standard language and an easy-to-understand structure makes the developing and coding process even faster. On the other hand, Python is for skilled developers.

Is Python Pandas worth learning? ›

Pandas is an essential package for Data Science in Python because it's versatile and really good at handling data. One component I really like about Pandas is its wonderful IPython and Numpy integration. This is to say, Pandas is made to be directly intertwined with Numpy just as peanut butter is to be with jelly.

What will replace Pandas? ›

Pandas Alternatives

We will look at Dask, Vaex, PySpark, Modin (all in python) and Julia. These tools can be split into three categories: Parallel/Cloud computing — Dask, PySpark, and Modin. Memory efficient — Vaex.

Should I use CSV or Pandas? ›

Pandas is better then csv for managing data and doing operations on the data. CSV doesn't provide you with the scientific data manipulation tools that Pandas does. If you are talking only about the part of reading the file it depends.

Is Python replacing Excel? ›

Nope, Python is a programming language and Excel is spreadsheet software. Python can enhance the capabilities of Excel. But can't replace it in any possible manner. No, but Python can enhance Excel's capabilities especially when working with larger data sets.

Can I master Python in 2 weeks? ›

It's possible to learn the basics of Python in two weeks of full-time study and practice, but it will likely take more time to gain enough experience working on projects to become truly proficient.

Can I master Python in 2 months? ›

In general, it takes around two to six months to learn the fundamentals of Python. But you can learn enough to write your first short program in a matter of minutes. Developing mastery of Python's vast array of libraries can take months or years.

Do hackers use Python to hack? ›

Exploit Writing: Python is a general-purpose programming language and used extensively for exploit writing in the field of hacking. It plays a vital role in writing hacking scripts, exploits, and malicious programs.

Is Python harder than Excel? ›

Python is harder to learn because you have to download many packages and set the correct development environment on your computer.

How many Excel rows can Python handle? ›

Current size limits for excel are 1,048,576 rows by 16,384 columns — owing to memory resources.

Is Anaconda needed for pandas? ›

Anaconda is the most used distribution platform for python & R programming languages in the data science & machine learning community as it simplifies the installation of packages like pandas, NumPy, SciPy, and many more.

How many hours does it take to learn Python? ›

From Awareness to Ability
GoalLearn Python's syntax and fundamental programming and software development concepts
Time RequirementApproximately four months of four hours each day
WorkloadApproximately ten large projects
1 more row

Is Python worth learning 2022? ›

As per the above discussion, Python is worth learning in 2022 as there are more job opportunities for python developers and it has been considered as the most demanding future language in the Tech world.

How much does a Python job cost? ›

3 months is enough if you want to start with a basic job. A basic job only requires you to know the basics of python. After learning the basic python programming, you will have to learn some advanced topics to be professional in it and have a job.

Can I learn Python at 45 and get a job? ›

For sure yes , if you have the desired skills and knowledge . No one will ever care about the age , there are plenty of jobs available in the field of python . Beside this you can also go for freelancing as an option.

Which Python job has highest salary? ›

Python Developer Salary, Python Jobs, Salary Information
  • Data Scientist: 78,456 USD/year.
  • DevOps Engineer: 97,310 USD/year.
  • Software Developer: 110,305 USD/year.
  • Senior Software Engineer: 90,596 USD/year.
  • Software Engineer: 90,662 USD/year.
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Can Python alone get me a job? ›

Yes, you can get a job by just knowing Python. Most of the machine learning programs are implemented using Python.

Which is easy NumPy or pandas? ›

Pandas has a better performance when a number of rows is 500K or more. Numpy has a better performance when number of rows is 50K or less. Indexing of the pandas series is very slow as compared to numpy arrays. Indexing of numpy Arrays is very fast.

Is pandas slow for large datasets? ›

Pandas is one of the most useful tools for data science, it makes data exploration, understanding, and manipulation intuitive and easy. That being said, Pandas seems to have one big drawback, it's SLOW.

Do data engineers use pandas? ›

Pandas is a powerful tool for cleaning, transforming, manipulating, or enriching data, among many other potential uses. As a result it has become a standard tool for data engineers for a wide range of applications.

Can pandas be used for big data? ›

pandas provides data structures for in-memory analytics, which makes using pandas to analyze datasets that are larger than memory datasets somewhat tricky. Even datasets that are a sizable fraction of memory become unwieldy, as some pandas operations need to make intermediate copies.

Do data analysts use pandas? ›

Aspiring data analysts and data scientists know that data wrangling is a vital step in any data analysis algorithm or machine learning project. Pandas, a powerful and widely used Python package, is used in data analysis and to perform data operations.

Is pandas a data science library? ›

4. Pandas. Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib.

Which is better pandas or Excel? ›

Speed - Pandas is much faster than Excel, which is especially noticeable when working with larger quantities of data. Automation - A lot of the tasks that can be achieved with Pandas are extremely easy to automate, reducing the amount of tedious and repetitive tasks that need to be performed daily.

Do I need Python for pandas? ›

Cython (writing C extensions for pandas) For many use cases writing pandas in pure Python and NumPy is sufficient. In some computationally heavy applications however, it can be possible to achieve sizable speed-ups by offloading work to cython.

Is Python enough for data engineering? ›

Python is also the go-to language for data scientists and a great alternative for specialist languages such as R for machine learning. Often branded the language of data, it's indispensable in data engineering.

Is Panda faster than database? ›

pandas is faster for the following tasks: groupby computation of a mean and sum (significantly better for large data, only 2x faster for <10k records) load data from disk (5x faster for >10k records, even better for smaller data)

How many GB can pandas handle? ›

The long answer is the size limit for pandas DataFrames is 100 gigabytes (GB) of memory instead of a set number of cells.

Should I learn pandas or PySpark? ›

In very simple words Pandas run operations on a single machine whereas PySpark runs on multiple machines. If you are working on a Machine Learning application where you are dealing with larger datasets, PySpark is a best fit which could processes operations many times(100x) faster than Pandas.

Why do Data Scientists prefer Python? ›

Thanks to Python's focus on simplicity and readability, it boasts a gradual and relatively low learning curve. This ease of learning makes Python an ideal tool for beginning programmers. Python offers programmers the advantage of using fewer lines of code to accomplish tasks than one needs when using older languages.

Do Data Scientists need Python? ›

Each language has its strengths and weaknesses. Both are widely used in the industry. Python is more popular overall, but R dominates in some industries (particularly in academia and research). For data science, you'll definitely need to learn at least one of these two languages.

Should I learn NumPy or pandas first? ›

First, you should learn Numpy. It is the most fundamental module for scientific computing with Python. Numpy provides the support of highly optimized multidimensional arrays, which are the most basic data structure of most Machine Learning algorithms. Next, you should learn Pandas.

Are pandas worth learning? ›

The answer is: Yes, mostly. 65% of pandas users want to continue using it in the coming year, which is very close to the levels Python had in 2022 (67%).

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