Analysis4.py

# coding: utf-8

# In[1]:

# get_ipython().magic('matplotlib inline')


# In[2]:

# importing required libraries
import os
import subprocess
import stat
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from datetime import datetime
sns.set(style="white")


# In[3]:

# absolute path till parent folder
abs_path = os.getcwd()
path_array = abs_path.split("/")
path_array = path_array[:len(path_array)-1]
homefolder_path = ""
for i in path_array[1:]:
    homefolder_path = homefolder_path + "/" + i 


# In[4]:

# path to clean data
clean_data_path = homefolder_path + "/CleanData/CleanedDataSet/cleaned_autos.csv"

# reading csv into raw dataframe
df = pd.read_csv(clean_data_path,encoding="latin-1")


# In[7]:

trial = pd.DataFrame()
for b in list(df["brand"].unique()):
    for v in list(df["vehicleType"].unique()):
        z = df[(df["brand"] == b) & (df["vehicleType"] == v)]["price"].mean()
        trial = trial.append(pd.DataFrame({'brand':b , 'vehicleType':v , 'avgPrice':z}, index=[0]))
trial = trial.reset_index()
del trial["index"]
trial["avgPrice"].fillna(0,inplace=True)
trial["avgPrice"].isnull().value_counts()
trial["avgPrice"] = trial["avgPrice"].astype(int)
trial.head(5)


# ## Average price of a vehicle by brand as well as vehicle type 

# In[11]:

# HeatMap tp show average prices of vehicles by brand and type together
tri = trial.pivot("brand","vehicleType", "avgPrice")
fig, ax = plt.subplots(figsize=(15,20))
sns.heatmap(tri,linewidths=1,cmap="YlGnBu",annot=True, ax=ax, fmt="d")
ax.set_title("Average price of vehicles by vehicle type and brand",fontdict={'size':18})
plt.show()


# In[12]:

fig.savefig((abs_path + "/Plots/heatmap-price-brand-vehicleType.png"))


# In[13]:

df.head(5)


# In[ ]: