Tuesday 19 May 2020

Pandas Built in Data Visualization | python crash course_05

Pandas Built in Data Visualization:

Welcome to python crash course tutorial, today we will see the last topic Pandas Built in Data Visualization in the data science section.
Data Visualizations is the presentation of data in graphical format. It help people understand the significance of data by summarizing and presenting a huge amount of data in a simple and easy-to-understand format and help communicate information clearly and effectively.
In this tutorial, we will learn about pandas built-in capabilities for data visualizations. It is built-off of matplotlib, but it baked into pandas for easier usage.
Let’s take a looks
Basic Plotting: plot

This functionality on Series and DataFrame is just a simple wrapper around the matplotlib libraries plot() methods.
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10,4),index=pd.date_range('1/1/2000',
periods=10), columns=list('ABCD'))
Basic Plotting
If the index consists of dates, it calls gct().autofmt_xdate() to format the x-axis as shown in the above illustration.
We can plots one column versus another using the x and y keywords.
Plotting method allow a handful of plot styles other than the default line plot. These method can be provided as the kind keyword argument to plots(). These include −
  1. bar or barh for bar plots
  2. hist for histogram
  3. box for boxplot
  4. 'area' for area plots
  5. 'scatter' for scatter plots
(Note: For detailed information please click here)
                                                               BEST OF LUCK!!!


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