London Housing Data
Analyse London Housing Dataset with Python
@kaggle.rohitgrewal_london_housing_data
Analyse London Housing Dataset with Python
@kaggle.rohitgrewal_london_housing_data
The dataset contains housing market information for different areas of London over time. It includes details such as average house prices, the number of houses sold, and crime statistics. The data spans multiple years and is organized by date and geographic area.
This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.
Using this dataset, we answered multiple questions with Python in our Project.
Q. 1) Convert the Datatype of 'Date' column to Date-Time format.
Q. 2.A) Add a new column ''year'' in the dataframe, which contains years only.
Q. 2.B) Add a new column ''month'' as 2nd column in the dataframe, which contains month only.
Q. 3) Remove the columns 'year' and 'month' from the dataframe.
Q. 4) Show all the records where 'No. of Crimes' is 0. And, how many such records are there ?
Q. 5) What is the maximum & minimum 'average_price' per year in england ?
Q. 6) What is the Maximum & Minimum No. of Crimes recorded per area ?
Q. 7) Show the total count of records of each area, where average price is less than 100000.
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These are the main Features/Columns available in the dataset :
Date – The month and year when the data was recorded.
Area – The London borough or area for which the housing and crime data is reported.
Average_price – The average house price in the given area during the specified month.
Code – The unique area code (e.g., government statistical code) corresponding to each borough or region.
Houses_sold – The number of houses sold in the given area during the specified month.
No_of_crimes – The number of crimes recorded in the given area during the specified month.
CREATE TABLE london_housing_data (
"date" TIMESTAMP,
"area" VARCHAR,
"average_price" BIGINT,
"code" VARCHAR,
"houses_sold" DOUBLE,
"no_of_crimes" DOUBLE
);
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