Assg24

import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df

count_unique = df['bedrooms'].nunique()                     # Apply unique function
print(count_unique)                                         # Print count of unique values

count_unique = df['date'].nunique()                     # Apply unique function
print(count_unique)                                         # Print count of unique values

df.columns

#  foematting dataset
# Round off the column values to two decimal places in python pandas
pd.options.display.float_format = '{:.2f}'.format
print(df)

df['bedrooms']=df['bedrooms'].astype(str)     #changing the data type of the bedrooms columns to string
after = df.dtypes
after

#  identifying the missing values
df.isnull().sum()

#  filling the missing value
replace_median = df['floors'].median()
print(replace_median)
print(df['floors'].fillna(replace_median, inplace=True))

#  filling the missing value
replace_mode = df['bedrooms'].mode()
print(replace_mode)
print(df['bedrooms'].fillna(replace_mode, inplace=True))

empty_rows= df[df['date'].isna()]
empty_rows