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Version 2

INTRODUCTION:

The objective of this project is to deliver insights to understand customer demands better and thus help developers to popularize the product. The dataset is chosen from Kaggle. It is of 10k Play Store apps for analyzing the Android market. It consists of in total of 10841 rows and 13 columns.

In [1]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

%matplotlib inline
In [2]:
google_playstore_df = pd.read_csv('googleplaystore.csv')
In [3]:
google_playstore_df.columns
Out[3]:
Index(['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type',
       'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver',
       'Android Ver'],
      dtype='object')
In [4]:
google_playstore_df.shape
Out[4]:
(10841, 13)
In [5]:
google_playstore_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 10841 entries, 0 to 10840 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 App 10841 non-null object 1 Category 10840 non-null object 2 Rating 9367 non-null float64 3 Reviews 10841 non-null int64 4 Size 10841 non-null object 5 Installs 10841 non-null object 6 Type 10840 non-null object 7 Price 10841 non-null object 8 Content Rating 10841 non-null object 9 Genres 10840 non-null object 10 Last Updated 10841 non-null object 11 Current Ver 10833 non-null object 12 Android Ver 10839 non-null object dtypes: float64(1), int64(1), object(11) memory usage: 1.1+ MB
In [6]:
google_playstore_df.describe()
Out[6]:
In [7]:
google_playstore_df['Last Updated'] = pd.to_datetime(google_playstore_df['Last Updated'])
In [8]:
google_playstore_df['Installs'] = google_playstore_df['Installs'].map(lambda x: x.rstrip('+'))
In [9]:
google_playstore_df['Installs'] = pd.to_numeric(google_playstore_df['Installs'].str.replace(',',''))
In [10]:
google_playstore_df['Price'] = pd.to_numeric(google_playstore_df['Price'].str.replace('$',''))
In [11]:
google_playstore_df['Category'] = google_playstore_df['Category'].str.replace(' ','')
In [12]:
# Row 10472 removed due to missing value of Category
google_playstore_df.drop(google_playstore_df.index[10472], inplace=True)
In [13]:
google_playstore_df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 10840 entries, 0 to 10840 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 App 10840 non-null object 1 Category 10840 non-null object 2 Rating 9366 non-null float64 3 Reviews 10840 non-null int64 4 Size 10840 non-null object 5 Installs 10840 non-null int64 6 Type 10839 non-null object 7 Price 10840 non-null float64 8 Content Rating 10840 non-null object 9 Genres 10840 non-null object 10 Last Updated 10840 non-null datetime64[ns] 11 Current Ver 10832 non-null object 12 Android Ver 10838 non-null object dtypes: datetime64[ns](1), float64(2), int64(2), object(8) memory usage: 1.2+ MB
In [14]:
google_playstore_df['log_installs'] = np.log2(google_playstore_df['Installs'])
C:\Users\SinghRit\Anaconda3\lib\site-packages\pandas\core\series.py:679: RuntimeWarning: divide by zero encountered in log2 result = getattr(ufunc, method)(*inputs, **kwargs)
In [15]:
boolean = google_playstore_df['App'].duplicated().any()
boolean
Out[15]:
True
In [16]:
google_playstore_df['App'].value_counts()
Out[16]:
ROBLOX                                               9
CBS Sports App - Scores, News, Stats & Watch Live    8
Duolingo: Learn Languages Free                       7
ESPN                                                 7
Candy Crush Saga                                     7
                                                    ..
Art Pixel Coloring. Color by Number.                 1
The dollar in mexico                                 1
Space Coast CU Mobile                                1
ECナビ×シュフー                                            1
GPS Speedometer, Distance Meter                      1
Name: App, Length: 9659, dtype: int64
As we can see from the above mentioned data, some of the apps are having multiple rows. Let's check out if there data is identical or not
In [17]:
google_playstore_df[google_playstore_df['App']=='ROBLOX']
Out[17]:
As we can see from the above dataframe, ROBLOX app is having identical rows with difference in number of reviews. It may have happened that for the same app, the data has been scraped in different points of time. So we have kept row of an app with maximum number of reviews, assuming it to be the latest one.

Dropping duplicate rows

In [18]:
google_playstore_df.drop_duplicates(inplace=True)

A single app is having multiple rows with difference in number of reviews. So, taking the row with highest number of reviews.

In [19]:
google_playstore_df = google_playstore_df.loc[google_playstore_df.groupby(['App'])['Reviews'].idxmax()]
In [20]:
sns.set_style('darkgrid')
plt.rcParams['font.size'] = 14
plt.rcParams['figure.figsize'] = (9, 5)
plt.rcParams['figure.facecolor'] = '#00000000'

Creating dataframe for top 20 Genres of applications

In [21]:
top_genres = google_playstore_df.Genres.value_counts().reset_index().rename(columns={'Genres':'Count','index':'Genres'})
In [22]:
top_genres
Out[22]:
In [23]:
genres_installs = google_playstore_df.groupby(['Genres'])[['Installs']].sum()
genres_installs
Out[23]:
In [24]:
top_genres_installs = pd.merge(top_genres, genres_installs, on='Genres')
top_20_genres_installs = top_genres_installs.head(20)
top_genres_installs
Out[24]:
In [25]:
plt.figure(figsize=(14,7))
plt.xticks(rotation=65)
plt.xlabel("Genres")
plt.ylabel("Number of application")
plt.title("Top 20 Genres")
sns.barplot(top_20_genres_installs.Genres, top_20_genres_installs.Count)
Out[25]:
<matplotlib.axes._subplots.AxesSubplot at 0x270c1fad308>

Dataframe from total installs in each Genres

In [26]:
plt.figure(figsize=(14,7))
plt.xticks(rotation=65)
plt.xlabel("Genres")
plt.ylabel("Installs")
plt.title("Installs according to Genres")
sns.barplot(top_20_genres_installs.Genres, top_20_genres_installs.Installs)
Out[26]:
<matplotlib.axes._subplots.AxesSubplot at 0x270c296d408>

Average rating

In [27]:
genres_ratings_df = google_playstore_df.groupby(['Genres'])[['Rating']].mean()
In [28]:
genres_ratings_df
Out[28]:
In [29]:
genres_installs_ratings = pd.merge(top_genres_installs, genres_ratings_df, on='Genres')
In [30]:
genres_installs_ratings.head()
Out[30]:

Best rated Genres

In [31]:
genres_installs_ratings.sort_values('Rating', ascending =False, inplace=True)
In [32]:
highest_rated_genres = genres_installs_ratings.iloc[0:20]
lowest_rated_genres = genres_installs_ratings.iloc[-20:]
lowest_rated_genres = lowest_rated_genres[lowest_rated_genres['Rating'].notnull()]
In [33]:
plt.figure(figsize=(14,7))
plt.xticks(rotation=65)
plt.xlabel("Genres")
plt.ylabel("Rating")
plt.title("Ratings according to Genres")
sns.barplot(highest_rated_genres.Genres, highest_rated_genres.Rating)
Out[33]:
<matplotlib.axes._subplots.AxesSubplot at 0x270c2835348>
In [34]:
plt.figure(figsize=(14,7))
plt.xticks(rotation=65)
plt.xlabel("Genres")
plt.ylabel("Rating")
plt.title("Ratings according to Genres")
sns.barplot(lowest_rated_genres.Genres, lowest_rated_genres.Rating)
Out[34]:
<matplotlib.axes._subplots.AxesSubplot at 0x270c2907c08>

Number of installs type wise according to Genres

In [35]:
google_playstore_df['Gaming Category App'] = google_playstore_df['Category']=='GAME'
In [36]:
categoty_type_installs = google_playstore_df.groupby(['Category','Type'])[['Installs']].sum().reset_index()
categoty_type_installs
Out[36]:
In [37]:
categoty_type_installs['log_Installs'] = np.log2(categoty_type_installs['Installs'])
categoty_type_installs
Out[37]:
In [38]:
# categoty_type_installs = categoty_type_installs[
#     categoty_type_installs['Genres'].isin(highest_rated_genres['Genres'].unique())
# ]
# categoty_type_installs
In [39]:
plt.figure(figsize=(14,7))
plt.xticks(rotation=65,fontsize=9)
plt.xlabel("Category")
plt.ylabel("Installs")
plt.title("Number of installs type wise according to Category")
sns.barplot('Category', 'log_Installs', hue='Type', data=categoty_type_installs);
In [40]:
# Converting KB to MB 
google_playstore_df['Size'] = google_playstore_df['Size'].apply(lambda x: str(x).replace('Varies with device', 'NaN') if 'Varies with device' in str(x) else x)
google_playstore_df['Size'] = google_playstore_df['Size'].apply(lambda x: str(x).replace('M', '') if 'M' in str(x) else x)
google_playstore_df['Size'] = google_playstore_df['Size'].apply(lambda x: str(x).replace(',', '') if 'M' in str(x) else x)
google_playstore_df['Size'] = google_playstore_df['Size'].apply(lambda x: float(str(x).replace('k', '')) / 1000 if 'k' in str(x) else x)
google_playstore_df['Size'] = google_playstore_df['Size'].apply(lambda x: float(x))

Installs v/s Size

In [41]:
plt.figure(figsize=(14,7))
sns.scatterplot(google_playstore_df['Size'], google_playstore_df['log_installs'], hue=google_playstore_df['Type'])
Out[41]:
<matplotlib.axes._subplots.AxesSubplot at 0x270c2907708>

Histogram of log installs

In [42]:
# to remove null values from log_installs column
google_playstore_df.loc[google_playstore_df['log_installs']==google_playstore_df['log_installs'].min(),'log_installs']=0
In [43]:
plt.title("Distribution of Logrithm of Installs")
plt.hist(google_playstore_df['log_installs']);

Histogram of size

In [44]:
# to remove null values from size column
google_playstore_df.loc[google_playstore_df['Size'].isnull(),'Size']=0
In [45]:
plt.title("Distribution of Size")
plt.hist(google_playstore_df['Size']);
In [46]:
user_reviews_df = pd.read_csv('googleplaystore_user_reviews.csv')

Merging dataframes on apps

In [47]:
merged_df = google_playstore_df.merge(user_reviews_df, on="App")

Category wise no.of positive and negetive reviews

In [48]:
category_sentiment = merged_df.groupby(['Category','Sentiment']).size().reset_index(name='Sentiment Count')
category_sentiment
Out[48]:
In [49]:
category_sentiment['log_sentiment_count'] = np.log2(category_sentiment['Sentiment Count'])
In [50]:
plt.figure(figsize=(14,7))
plt.xticks(rotation=65,fontsize=9)
plt.xlabel("Category")
plt.ylabel("Installs")
plt.title("Number of installs type wise according to Genres")
sns.barplot('Category', 'log_sentiment_count', hue='Sentiment', data=category_sentiment);

Histogram of subjectivity

In [51]:
# bb = merged_df[merged_df['Sentiment_Subjectivity']>0.5]
# bb
In [52]:
# cc = bb.groupby(['Category'])['Sentiment_Subjectivity'].mean().reset_index(name='Subjectivity Aggregate')
In [53]:
plt.title("Distribution of Size")
plt.hist(merged_df[merged_df['Sentiment_Subjectivity'].notnull()]['Sentiment_Subjectivity'])
plt.show()

Does sentiment_subjectivity proportional to sentiment_polarity

In [54]:
sentimet_subjectivity_polarity = merged_df.groupby(['Category'])['Sentiment_Polarity','Sentiment_Subjectivity'].mean().reset_index().rename(columns={'Sentiment_Polarity':'Sentiment_polarity_avg','Sentiment_Subjectivity':'sentiment_subjectivity_avg'})
C:\Users\SinghRit\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead. """Entry point for launching an IPython kernel.
In [55]:
plt.figure(figsize=(14,7))
plt.xticks(rotation=65,fontsize=9)
plt.plot(sentimet_subjectivity_polarity['Category'], sentimet_subjectivity_polarity['Sentiment_polarity_avg'], 's-b')
plt.plot(sentimet_subjectivity_polarity['Category'], sentimet_subjectivity_polarity['sentiment_subjectivity_avg'], 'o--r')

# plt.xlabel('Year')
# plt.ylabel('Yield (tons per hectare)')

plt.title("Does sentiment_subjectivity proportional to sentiment_polarity")
plt.legend(['Sentiment_polarity_avg','sentiment_subjectivity_avg']);
In [56]:
plt.figure(figsize=(14,7))
sns.scatterplot(merged_df['Sentiment_Subjectivity'], merged_df['Sentiment_Polarity'])

# plt.xlabel('Year')
# plt.ylabel('Yield (tons per hectare)')

plt.title("Does sentiment_subjectivity proportional to sentiment_polarity")
Out[56]:
Text(0.5, 1.0, 'Does sentiment_subjectivity proportional to sentiment_polarity')