%matplotlib inline ensures that the plotted figures show up correctly in the notebook when a cell is run. Nifty_pharma = pd.read_csv( 'NIFTY PHARMA.csv',parse_dates=) Nifty_IT = pd.read_csv( 'NIFTY IT.csv',parse_dates=) 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. Users can upload data, create and label the plot and export the figure in common file formats.Data Visualisation is an essential step in any data science pipeline. A convenient Web-based tool to create customized box plots is available at (ref. As further variations are possible 3, it is crucial to always annotate the range of the whiskers. Alternatively, the minimum and maximum value in the data set are used as end points for the whiskers. Each outlier outside the whiskers is represented by an individual mark. The whiskers are lines extending from Q1 and Q3 to end points that are typically defined as the most extreme data points within Q1 − 1.5 × IQR and Q3 + 1.5 × IQR, respectively. A line across the box indicates the median. The box ranges from the first (Q1) to the third quartile (Q3) of the distribution and represents the interquartile range (IQR). 2b,c), providing an effective summary of a potentially large amount of data 2. ![]() If the quantities add up to the same total for each item, then a grouped bar chart is equivalent to multiple pie charts, yet a grouped bar chart affords more accurate readings of values and comparisons.īox plots, also known as box-and-whiskers plots, encode five characteristics of a distribution by position and length ( Fig. However, if our primary goal is to enable comparisons of values across categories within each item while still enabling comparisons across items, then a grouped bar chart ( Fig. Comparisons within each category are more accurate in layered bar charts than in stacked bar charts because layered bar charts provide a common baseline for the values in each category. If, instead of the distribution of the overall quantities, we are primarily interested in the distribution of values in each category across all items, a layered bar chart ( Fig. A common application for stacked bar charts is to visualize rankings that are derived from multiple attributes 1. 1b) are the best choice if we are primarily interested in comparing the overall quantities across items but also want to illustrate the contribution of each category to the totals. There are several options to visualize such data using bar charts. Often the counts that we want to represent are sums over multiple categories. Bar charts encode quantities by length, which is a highly accurate visual encoding and preferred over the angle-based strategy used in pie charts ( Fig.
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