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"""
Spyder Editor
This is a temporary script file.
"""
import pandas as pd;
#1. Read the data from Data.csv to DataFrame “dataset”.
df = pd.read_csv('D:Projects_sem9MachineLearningData.csv')
print(df.to_string())
#2. Display total number of rows and columns of datase.
row, col = df.shape
print("n,Toatl row:",row)
print("n,Toatl columns:",col)
#3.Display first 5 record from dataset.
print("n",df.head(5))
#4. Display all values of column “City” by accessing column name as attribute of dataset.
print("n",df[["City"]])
#5. Display all values of column “City” by using indexing ( [ ] ) operator.
demo = pd.DataFrame(df[["City"]], index = [1,2,3,4,5,6,7,8,9,10])
print("n",demo)
#6. Display “City” value from the first record.
demo1 = pd.DataFrame(df[["City"]], index = [1])
print("n",demo1)
#7. Display first record using iloc( ).
demo2 = df.iloc[0]
print("n",demo2)
#8. Display all values of first column using iloc( ).
demo3 = df.iloc[:]
print("n",demo3)
#9. Display “City” value from 2nd and 3rd records using iloc().
demo4 = pd.DataFrame(df.iloc[[2,3],[0]])
print("n",demo4)
#10. Display “City” value from 2nd and 4th records using iloc( ).
demo4 = pd.DataFrame(df.iloc[[2,4],[0]])
print("n",demo4)
#11. Display last 3 records from the dataset using iloc( ).
demo5 = df.tail(3)
print("n",demo5)
#12. Display all records using loc( ) where “City” value is “Bardoli”.
print("n",df.loc[df['City'] == "Bardoli"])
#13. Display all “City” values with number of records having that value using groupby( ).
print("n",df.groupby('City')['City'].count())
#14. Display all records where “Age” is NULL.
print("n",df[df['Age'].isna()==True])
#15. Delete all rows with missing values
print("n",df.dropna())
#16. Delete all columns with missing values.
print("n",df.dropna(axis=1))
#17. Replace missing categorical values with most frequent values
result = df.fillna(df.mode().iloc[0])
print(result)
#18. Replace missing “City” name with value “Bardoli”, if any
print("n",df['City'].fillna(value="Barodoli"))
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