My little collection of Python recipes for data science featuring Pandas, Matplotlib, and friends.
Pandas: reading a CSV from a string
In Pandas we can load data from a CSV file with read_csv:
Python is the top dog when it comes to data science for now and in the foreseeable future. Knowledge of NumPy, one of its most powerful libraries is often a requirement for Data Scientists today. Use this cheat sheet as a guide in the beginning and come back to it when needed, and you’ll be well on your way to mastering the NumPy library. A detailed Python cheat sheet with key data types, functions, and commands you should learn as a beginner. Free to download as PDF and PNG. Free resource for helping beginners to build, manage and grow their websites. Guest blog post by Mirko Krivanek Below is a Python for Visualization cheat sheet, originally published here as an infographics. Other cheat sheets about Data Science, Python, Visualization, and R, can be found here. Here are additional resources Infographics Dashboards R Python Excel Visualization Cowplot (see illustration at the bottom) Enjoy! DSC Resources Career: Training Books Cheat. Python For Data Science Cheat Sheet Pandas Basics Learn Python for Data Science Interactively at www.DataCamp.com Pandas DataCamp Learn Python for Data Science Interactively Series DataFrame 4 Index 7-5 3 d c b A one-dimensional labeled array a capable of holding any data type Index Columns A two-dimensional labeled data structure with columns.
Python Cheat Sheet for Data Science Pandas, Numpy, and Scikit-Learn are among the most popular libraries for data science and analysis with Python. Numpy is used for lower level scientific computation. Pandas is built on top of Numpy and designed for practical data analysis in Python.
Now, it's not uncommon to have some tabular data as a string:
To load this string as a file we can use Python built-in StringIO:
Mark stern jll. Credits to my friend Ernesto for this tip.
How to plot a CSV with Pandas
Consider this file-like CSV:
To plot this CSV with Pandas we call the plot method on the DataFrame:
To show the plot instead we call show on plt:
You can also save the plot with savefig:
Now, you'll notice that the resulting picture has indeed two labels taken from the CSV column. But the x axis is associated with the indexes of each DataFrame row:
A DataFrame in fact has indexes:
To use the year column instead of an index for the x axis we can instruct plot respectively with the x and y arguments (in this example you can omit y):
Now the plot is coherent with the dataset:
How to groupby in Pandas
Python Data Science Cheat Sheet Pdf
Suppose you've got a CSV with two columns, year and amount:
Simplimatic 8010 manual. To compute the amount by year you can group by year and then call sum:
Python For Data Science Cheat Sheet Scikit Learn
This gives you a new DataFrame as expected: