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Use these commands to perform a variety of data cleaning tasks.ĭf.columns = | Rename columns Use these commands to select a specific subset of your data.ĭf | Returns column with label col as Seriesĭf] | Returns columns as a new DataFrameĭf.iloc | First element of first column Data Cleaning S.value_counts(dropna=False) | View unique values and countsĭf.apply(pd.Series.value_counts) | Unique values and counts for all columns Selection Use these commands to take a look at specific sections of your pandas DataFrame or Series.ĭf.head(n) | First n rows of the DataFrameĭf.tail(n) | Last n rows of the DataFrameĭf.info() | Index, Datatype and Memory informationĭf.describe() | Summary statistics for numerical columns Pd.Series(my_list) | Create a series from an iterable my_listĭf.index = pd.date_range('0', periods=df.shape) | Add a date index Viewing/Inspecting Data Pd.DataFrame(np.random.rand(20,5)) | 5 columns and 20 rows of random floats These commands can be useful for creating test segments. xlsx, SQL, or JSON.ĭf.to_csv(filename) | Write to a CSV fileĭf.to_excel(filename) | Write to an Excel fileĭf.to_sql(table_name, connection_object) | Write to a SQL tableĭf.to_json(filename) | Write to a file in JSON format Create Test Objects Use these commands to export a DataFrame to CSV. Pd.DataFrame(dict) | From a dict, keys for columns names, values for data as lists Exporting Data Pd.read_clipboard() | Takes the contents of your clipboard and passes it to read_table()
Pd.read_html(url) | Parses an html URL, string or file and extracts tables to a list of dataframes Pd.read_json(json_string) | Read from a JSON formatted string, URL or file. Pd.read_sql(query, connection_object) | Read from a SQL table/database Pd.read_excel(filename) | From an Excel file Pd.read_table(filename) | From a delimited text file (like TSV) Use these commands to import data from a variety of different sources and formats. In this cheat sheet, we’ll use the following shorthand:Īs 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.Īlso, 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 pd
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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. Pandas Cheat Sheet: Guideįirst, 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.
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In our Python datetime tutorial, for example, you’ll also learn how to work with dates and times in pandas.
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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.
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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! 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. 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! If you haven’t learned any pandas yet, we’d strongly recommend working through our pandas course. That’s why we’ve created a pandas cheat sheet to help you easily reference the most common pandas tasks.īefore we dive into the cheat sheet, it’s worth mentioning that you shouldn’t rely on just this. But even when you’ve learned pandas - perhaps in our interactive pandas course - it’s easy to forget the specific syntax for doing something. If you’re interested in working with data in Python, you’re almost certainly going to be using the pandas library. MaPandas Cheat Sheet - Python for Data Science
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