![]() ![]() You need this chart to determine the actual patterns of your growth indicators (metrics). In the second example, Google Colab is used for writing the Python program and making the scatter plot using the same library and the same data set.A Scatter Plot is one of the charts data visualization experts use to determine the general trend of variables in a data set. Then a Python program is written to make a scatter plot using the functions from Plotly. First, the method is given where the dataset from Kaggle is downloaded and saved for analysis. ![]() In this Python and Plotly article, by two different examples, the ways to show how to make a scatter plot using the Python library called Plotly are given. Printing the Result and Showing the Scatter Plot print(dff.head())įigg = pxx.scatter(dff, x="petal_length", y="sepal_length") Including the Libraries and Reading the CSV file import pandas as pdd Run the Python file in the Command WindowĮxample 1: Making the Scatter Plot Using Python on Google Colab Uploading the data, CSV file #Uploading the csv #This library is needed to make the scatter plotįigg = pxx.scatter(dff, x="sepal_width", y="petal_width")įigg.update_traces(marker=dict(size=12, line=dict(width=2, color="red")), selector=dict(mode='markers')) Write the Following Code in This File #include the required libraries Login to Kaggle and download the CSV file from this link − To make the scatter plot, we will use the data available on Kaggle. Check the result as it will be displayed in the colab notebook.Įxample 1: Making the Scatter Plot Using Python and Plotly Saving the data File / csv File Required for data Analysis Run the program by clicking the playbotton on the given code cell. Step 6 − Write the function to show the scatterplot. Step 5 − Make a scatter plot, using the scatter() function, and specify the petal_length for x axis and sepal_length on y axis. Step 4 − Make a dataframe dff and show the columns and content of this dataframe. Plotly is the open-source graphing library for Python that will be used for making scatter plots. Step 2 − Upload the IRIS.csv file that was downloaded from Kaggle and saved, using the link given in the example 1, as the dataset given here will be used for making the scatter plot. Open a new Colab Notebook and write the Python code in it. The plot will open in a new tab in the browser.Įxample 2: Making the Scatter Plot Using Python and Plotly on Google Colab Design Steps and Coding Step 5 − Set the style of the marker such as size and color. Step 4 − Make a scatter plot, using the scatter() function, and specify the sepal_width for x axis and petal_width on y axis. Step 3 − Make a dataframe dff and show the columns and content of this dataframe. Step 2 − Now read the IRIS.csv file as the dataset given here will be used for making the scatter plot. Example 1: Making the Scatter Plot Using Python and Plotly Design Steps and Coding Out of these, we will use sepal_width, and petal_width for the scatter plot in Example 1 and we will use sepal_length, and petal_length for the scatter plot in Example 2. This CSV file contains five columns named sepal_length, sepal_width, petal_length, petal_width, and species. The IRIS.csv file Used sepal_length,sepal_width,petal_length,petal_width,species In both these examples, the open-source dataset from Kaggle is used for the data analysis and visualizations. In another example, using the Google Colab the method is shown where without having Python installed in the computer, Python and Plotly can still be used and a scatter plot can be made. In the first example, the Python installed in the computer system is used to run a Python program that is written for making a scatter plot. In this article, using two different examples, this Python library called Plotly is used with Python code to make the scatter plots. Plotly is a nice open-source graphing library that can be used with Python and is used for making a variety of plots and charts quickly and easily. Sometimes, the task is to analyze a dataset and use charts or plots for data visualization. ![]()
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