1. Lernen
    2. /
  2. Kurse
    3. /
  3. Python for R Users
  • 1

    The Basics

    Kostenlos

    Learn about some of the most important data types (integers, floats, strings, and booleans) and data structures (lists, dictionaries, numpy arrays, and pandas DataFrames) in Python and how they compare to the ones in R.

  • 2

    Control flow, Loops, and Functions

    This chapter covers control flow statements (if-else if-else), for loops and shows you how to write your own functions in Python!

  • 3

    Pandas

    In this chapter you will learn more about one of the most important Python libraries, Pandas. In addition to DataFrames, pandas provides several data manipulation functions and methods.

  • 4

    Plotting

    You will learn about the rich ecosystem of visualization libraries in Python. This chapter covers matplotlib, the core visualization library in Python along with the pandas and seaborn libraries.

  • 5

    Capstone

    As a final capstone, you will apply your Python skills on the NYC Flights 2013 dataset.

Initializing

Ubung

Univariate plots in seaborn

Seaborn is a popular plotting library. It embraces the concepts of "tidy data" and allows for quick ways to plot multiple variables.

You will begin by generating univariate plots. Barplots and histograms are created using the countplot() and histplot() functions, respectively. 

import seaborn as sns
import matplotlib.pyplot as plt

# Bar plot
sns.countplot(x='column_name', data=df)
plt.show()

# Histogram
sns.histplot(df['column_name'], kde = True)
plt.show()

Anweisungen 1/2

undefined XP
    1
    2

Use the seaborn countplot() function to draw a bar plot of sex.