![]() Nifty_fmcg = pd.read_csv( 'NIFTY FMCG.csv',parse_dates=) Nifty_bank = pd.read_csv( 'NIFTY BANK.csv',parse_dates=) Let’s import the necessary libraries and the extracted dataset required for visualization: # Importing required modules import pandas as pd You can download the sample dataset from here. The dataset is openly available on Kaggle, but we’ll be using a subset of the data containing the stock value of only four sectors – banking, pharma, IT, and FMCG. NIFTY 50 stands for National Index Fifty, and represents the weighted average of 50 Indian company stocks in 17 sectors. The NIFTY 50 index is the National Stock Exchange of India’s benchmark for the Indian equity market. We’re going to work with the NIFTY-50 dataset. MIGHT BE USEFULĬheck this Neptune-pandas integration that lets you log pandas dataframes to Neptune. Pandas Plot simplifies the creation of graphs and plots, so you don’t need to know the details of working with matplotlib.īuilt-in visualization in pandas really shines in helping with fast and easy plotting of series and DataFrames. The Pandas Plot is a set of methods that can be used with a Pandas DataFrame, or a series, to plot various graphs from the data in that DataFrame. Think of matplotlib as a backend for pandas plots. These plotting functions are essentially wrappers around the matplotlib library. Pandas objects come equipped with their plotting functions. In this article, we’ll look at how to explore and visualize your data with pandas, and then we’ll dive deeper into some of the advanced capabilities for visualization with pandas. Plotting with pandas is pretty straightforward. There’s also pandas, which is mainly a data analysis tool, but it also provides multiple options for visualization. These libraries are intuitive and simple to use. There are several useful libraries for doing visualization with Python, like matplotlib or seaborn. Exploring your data visually opens your mind to a lot of things that might not be visible otherwise. In this plot, the x-axis measures the amount of money spent by a country on elementary and secondary education per child.Data Visualisation is an essential step in any data science pipeline. Typically, the x-axis has numbers representing different time periods or names of things being measured. In x-y plots, like the one above, the x-axis runs horizontally (flat). In this graph, two sets of data are presented. Line graphs can present more than one group of data at a time. The most important part of your graph is the information, or data, it contains. In this line graph, the y-axis is measuring the Gross Domestic Product (GDP) of each country. The y-axis usually starts counting at 0 and can be divided into as many equal parts as you want to. Typically, the y-axis has numbers for the amount of stuff being measured. In x-y plots, the y-axis runs vertically (up and down). It is important to give credit to those who collected your data! In this graph, the source tells us that we found our information from the Organization for Economic Cooperation and Development. The source explains where you found the information that is in your graph. Each of the colors in this legend represents a different country. Just like on a map, the legend helps the reader understand what they are looking at. ![]() The legend tells what each point represents. The title of this graph tells the reader that the graph contains information about the difference in money spent on students of elementary and secondary schools from different countries. It can be creative or simple as long as it tells what is in the graph. This helps the reader identify what they are about to look at. The title offers a short explanation of what is in your graph.
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