https://www.kaggle.com/

Step 1 - Additional Libs

import seaborn as sns # high level interface for drawing attractive statistical graphics (based on matplotlib)

import matplotlib.pyplot as plt # 2D ploting of graphs

Step 2 - Add data set and copy the link

Step 3 –

df=pd.read_csv("../input/students-performance-in-exams/exams.csv") # defining the path for reading the data set

df.head() # reading 1^st 5 data sets from the file

Step 4 –

df.tail(10) # reading 1^st 10 data sets from the file

Step 5 –

df.describe() # will get mean, max, min with counts

Step 6 –

df.info() # to get count and data types details

Step 7 -

df.dtypes # data types details

Step 8 –

df["gender"].value_counts() # gives the counts from column

df["lunch"].value_counts() # gives the counts from column

Step 9 –

df_rank = df.groupby(['writing score'])

df_rank.mean()

Step 10 –

sns.distplot(df["reading score"]) #  The distplot represents the univariate distribution of data i.e. data distribution of a variable against the density distribution

Step 11 -

df["gender"].value_counts().plot(kind="bar")

Step 12 -

df["parental level of education"].value_counts().plot(kind="pie",title="Parental education level")

Step 13 –

sns.pairplot(df,hue="gender")