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<speak> For data visualizations, we use matplotlib.<break strength="x-strong"/> Matplotlib is useful in visualizing the data.<break strength="x-strong"/> In some cases, the direct numerical analysis may not be helpful. <break strength="strong"/>In that case, we will use the data visualizations. <break strength="strong"/>Data visualizations are very helpful in understanding the data. <break strength="strong"/>Hence we require these three libraries for analyzing and visualizing the data.<break strength="x-strong"/> Now import pandas library using import Pandas as pd. <break strength="strong"/>You have imported Pandas as pd into the current python environment. <break strength="strong"/>So whenever you want to access the methods available in the pandas' library, you can use the name pd instead of pandas. <break strength="strong"/>Similarly, import the NumPy library and give it a valid name.<break strength="x-strong"/> Similarly, from matplotlib, import pyplot and give it a name plt.<break strength="x-strong"/> Hence all the methods in the libraries are available in the current environment. <break strength="strong"/>So we can use methods once imported.<break strength="x-strong"/> This percentage matplotlib inline is useful to display the plots in Jupyter Notebook.<break strength="x-strong"/> Next, read underscore csv.<break strength="x-strong"/> Graduate admission data is available in the graduate_admission.csv file. <break strength="strong"/>This csv file contains student's profiles, university rankings, and the chance of admission. <break strength="strong"/>You need to store this data into some variable.<break strength="x-strong"/> We use the read_csv function present in pandas to read or load data from CSV files. <break strength="strong"/>By using this function, we can copy data to a data frame variable.<break strength="strong"/> Here the data frame variable name is data.<break strength="x-strong"/> Now we can do any operations on this data frame data using pandas and NumPy.<break strength="x-strong"/> Data is available in rows and columns format. <break strength="strong"/>You can get information about the columns present in the data frame using data.columns method. <break strength="strong"/>Each column refers to a variable. <break strength="strong"/>By using data.columns, you will get the list of the columns available in the dataset or the csv file.<break strength="x-strong"/> There are nine columns. <break strength="strong"/>The first column is the serial number, the second column is GRE score, the third column TOEFL score, University rating, the strength of purpose, LOR, CGPA, Research, and Chance of admitting.<break strength="x-strong"/> To see the first five rows, use the data.head().<break strength="x-strong"/> </speak>