Activity

Author: nursnaaz

Chapter 3/.DS_Store

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+#!/usr/bin/env python
+# coding: utf-8
+# Author : Mohamed Noordeen
+# Date : 22-04-2019
+
+
+#import Library
+import pandas as pd
+
+
+# 1.Load the Data
+
+
+df = pd.read_csv("Wholesale customers data.csv")
+
+
+# 2.Understand the Data Features
+
+
+
+#Finding number of rows and columns
+print("Number of rows and columns : ",df.shape)
+
+
+
+#Basic Information about all the columns
+print("Basic Information about all the column : ")
+print(df.info())
+
+
+
+#Basic Statistics about all the columns
+print("Basic Statistics about all the column : ")
+print(df.describe().transpose())
+
+
+# 3.Check for NULL values and their datatypes
+
+
+
+#checking for any null in each column
+null_ = df.isna().any()
+print(null_)
+
+
+
+#findind datatypes of each column
+dtypes = df.dtypes
+print(dtypes)
+
+
+
+#Combining both null and datatypes of each column
+info = pd.concat([null_,dtypes],axis = 1,keys = ['Null','type'])
+print(info)
+
+
+# 4.Remove the missing values (if any)
+# 
+# Since there are no missing values in the data set we have nothing to handle
+
+
+
+#finding the data types of each column and checking for null
+null_ = df.isna().any()
+dtypes = df.dtypes
+info = pd.concat([null_,dtypes],axis = 1,keys = ['Null','type'])
+print(info)
+
+
+# 5.Perform Feature Scaling
+
+from sklearn import preprocessing
+ 
+std_scale = preprocessing.StandardScaler().fit(df)
+df_std = pd.DataFrame(std_scale.transform(df),columns=df.columns)
+print("Standardised data : \n",df_std)
+