Data_Insight.txt - Get the Data df =...

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# Get the Data df = pd.read_csv('24_Uberdrive.csv') #View the first 5 rows of data df.head(5) #View the last 5 rows of data df.tail() # understand shape and size of data print(df.shape) print(df.size) #Get a summary of the numerical columns in the data df.describe() #get more information about data #Get the number of missing values in each column df.isnull().sum() # Get the initial data with dropping the NA values df = df.dropna() #Get the shape of the dataframe after removing the null values df.shape #get the summary of data df.describe() ------------------------------------------------------------- Lets explore the data parameter wise - 1.Destination - (starting and stopping) 2.Time - (hour of the day, day of week, month of year) 3.Categories 4.Purpose 5.Grouping two parameters to get more insights --------------------------------------------------------------- # Get the starting destination, unique destination
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Unformatted text preview: print(df['START*'].unique()) #names of unique start points print(len(df['START*'].unique())) #count of unique start points # Get the starting destination, unique destination print(df['STOP*'].unique()) #names of unique start points print(len(df['STOP*'].unique())) #count of unique start points #Identify popular start destinations - top 10 df['START*'].value_counts().head(10) #Identify popular stop destinations - top 10 df['STOP*'].value_counts().head(10) #Find out most farthest start and stop pair -top10 #Dropping Unknown Location Value df2 = df[df['START*']!= 'Unknown Location'] df2 = df2[df2['STOP*']!= 'Unknown Location'] df2.groupby(['START*','STOP*']) ['MILES*'].sum().sort_values(ascending=False).head(10) #Find out most popular start and stop pair - top10 df2.groupby(['START*','STOP*']).size().sort_values(ascending=False).head(10)...
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  • Fall '19
  • Column, Start, Stop consonant, START I

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