Challenge

Project 1

AAshfaq Ali15 Jul 2022

-- Data cleaning on Automobile 1985 dataset and perform descriptive analytics

I have worked on the dataset for conversion of raw data into a form that will make it easy to understand & interpret, ie., rearranging, ordering, and manipulating data to provide insightful information about the provided data.

The dataset is about cars from 1985, It consists of:

Number of Instances: 205

Number of Attributes: 26 total

15 continuous

1 integer

10 nominal

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

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

Renaming the columns:

df.columns = ["symboling", "normalized-losses", "make", "fuel", "aspiration", "number of doors", "body-style", "drive-wheels", "engine-location",
"wheel-base","length", "width", "height", "curb-weight", "engine-type", "num-of-cylinders", "engine-size", "fuel-system", "bore",
"stroke", "compression-ratio", "horsepower", "peak-rpm", "city-mpg", "highway-mpg", "price"]

#Display the column names
print(list(df.columns))

df.describe()

df = df.replace('?', np.NAN) # Replacing the empty(?) values with NaN.

df.head()

df.isnull().sum()

symboling             0
normalized-losses    40
make                  0
fuel                  0
aspiration            0
number of doors       2
body-style            0
drive-wheels          0
engine-location       0
wheel-base            0
length                0
width                 0
height                0
curb-weight           0
engine-type           0
num-of-cylinders      0
engine-size           0
fuel-system           0
bore                  4
stroke                4
compression-ratio     0
horsepower            2
peak-rpm              2
city-mpg              0
highway-mpg           0
price                 4
dtype: int64


sns.heatmap(df.isnull(),yticklabels="False",cmap='viridis')


## Replacing NaN values with mean in each column

### Replacing the NaN values with mean for each column which has null values

df[["price"]]=df[["price"]].astype(str).astype(float)
mean = df[["price"]].mean(axis=0, skipna= True)
#print('mean value is: ',mean)
df[["price"]] = df[["price"]].replace(np.NaN, mean)

df[["normalized-losses"]]=df[["normalized-losses"]].astype(str).astype(float)
mean = df[["normalized-losses"]].mean(axis=0, skipna= True)
#print('mean value is: ',mean)
df[["normalized-losses"]] = df[["normalized-losses"]].replace(np.NaN, mean)

df[["bore"]]=df[["bore"]].astype(str).astype(float)
mean = df[["bore"]].mean(axis=0, skipna= True)
#print('mean value is: ',mean)
df[["bore"]] = df[["bore"]].replace(np.NaN, mean)

df[["stroke"]]=df[["stroke"]].astype(str).astype(float)
mean = df[["stroke"]].mean(axis=0, skipna= True)
#print('mean value is: ',mean)
df[["stroke"]] = df[["stroke"]].replace(np.NaN, mean)

df[["horsepower"]]=df[["horsepower"]].astype(str).astype(float)
mean = df[["horsepower"]].mean(axis=0, skipna= True)
#print('mean value is: ',mean)
df[["horsepower"]] = df[["horsepower"]].replace(np.NaN, mean)

df[["peak-rpm"]]=df[["peak-rpm"]].astype(str).astype(float)
mean = df[["peak-rpm"]].mean(axis=0, skipna= True)
#print('mean value is: ',mean)
df[["peak-rpm"]] = df[["peak-rpm"]].replace(np.NaN, mean)

### Here in "number of doors" columns there is string values which needs to be replaced by integers using a mapping function

df=df.drop(['number of doors'],axis=1) #Drop the column of "number of doors"

print(df.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 204 entries, 0 to 203
Data columns (total 26 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   symboling          204 non-null    int64  
 1   normalized-losses  204 non-null    float64
 2   make               204 non-null    object 
 3   fuel               204 non-null    object 
 4   aspiration         204 non-null    object 
 5   body-style         204 non-null    object 
 6   drive-wheels       204 non-null    object 
 7   engine-location    204 non-null    object 
 8   wheel-base         204 non-null    float64
 9   length             204 non-null    float64
 10  width              204 non-null    float64
 11  height             204 non-null    float64
 12  curb-weight        204 non-null    int64  
 13  engine-type        204 non-null    object 
 14  num-of-cylinders   204 non-null    object 
 15  engine-size        204 non-null    int64  
 16  fuel-system        204 non-null    object 
 17  bore               204 non-null    float64
 18  stroke             204 non-null    float64
 19  compression-ratio  204 non-null    float64
 20  horsepower         204 non-null    float64
 21  peak-rpm           204 non-null    float64
 22  city-mpg           204 non-null    int64  
 23  highway-mpg        204 non-null    int64  
 24  price              204 non-null    float64
 25  mapping_nod        204 non-null    float64
dtypes: float64(12), int64(5), object(9)
memory usage: 41.6+ KB
None

In [115]:




df.isnull().sum()

symboling            0
normalized-losses    0
make                 0
fuel                 0
aspiration           0
body-style           0
drive-wheels         0
engine-location      0
wheel-base           0
length               0
width                0
height               0
curb-weight          0
engine-type          0
num-of-cylinders     0
engine-size          0
fuel-system          0
bore                 0
stroke               0
compression-ratio    0
horsepower           0
peak-rpm             0
city-mpg             0
highway-mpg          0
price                0
mapping_nod          0
dtype: int64


## Exploratory Data Analysis (EDA)

## Heat map

## Scatter plots

Project 1

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