Statement of Completion#b878c06c
FELLAHI ABDERRAOUF has successfully completed this project.
Data Cleaning with Pandas
medium
Capstone Project: Cleaning Google Playstore data
Resolution
Activities
In [63]:
import pandas as pd
import numpy as np
import seaborn as sns
import missingno as msno
In [64]:
df = pd.read_csv('googleplaystore.csv')
df.sample(5)
Out[64]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10682 | Fruit Ninja Classic | GAME | 4.3 | 85468 | 36M | 1,000,000+ | Paid | $0.99 | Everyone | Arcade | 8-Jun-18 | 2.4.1.485300 | 4.0.3 and up |
| 731 | English words application mikan | EDUCATION | 4.7 | 9888 | Varies with device | 500,000+ | Free | 0 | Everyone | Education | 30-Jul-18 | Varies with device | 4.1 and up |
| 7256 | Wallpapers DAF CF Trucks | PERSONALIZATION | NaN | 1 | 13M | 100+ | Free | 0 | Teen | Personalization | 13-Jun-16 | 1 | 2.3.3 and up |
| 872 | Cinematic Cinematic | ENTERTAINMENT | 4.4 | 37000 | 15M | 1,000,000+ | Free | 0 | Mature 17+ | Entertainment | 21-Jun-18 | 4.0.5 | 4.1 and up |
| 8643 | Wunderlist: To-Do List & Tasks | PRODUCTIVITY | 4.6 | 404610 | Varies with device | 10,000,000+ | Free | 0 | Everyone | Productivity | 6-Apr-18 | Varies with device | Varies with device |
Data Cleaning¶
1. Which of the following column(s) has/have null values?¶
In [65]:
msno.bar(df)
Out[65]:
<Axes: >
In [66]:
df.info() #we can't get much from the bar plot so we use numerical info instead
#reviews have also some issues in fact when we print the data frame it only shows numbers but the type of the column is object
<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 10841 non-null object 2 Rating 9367 non-null float64 3 Reviews 10841 non-null object 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 10840 non-null object 9 Genres 10841 non-null object 10 Last Updated 10841 non-null object 11 Current Ver 10833 non-null object 12 Android Ver 10838 non-null object dtypes: float64(1), object(12) memory usage: 1.1+ MB
In [67]:
df.isnull().sum().sort_values(ascending=False)
Out[67]:
Rating 1474 Current Ver 8 Android Ver 3 Type 1 Content Rating 1 App 0 Category 0 Reviews 0 Size 0 Installs 0 Price 0 Genres 0 Last Updated 0 dtype: int64
In [68]:
df.isnull().any()
Out[68]:
App False Category False Rating True Reviews False Size False Installs False Type True Price False Content Rating True Genres False Last Updated False Current Ver True Android Ver True dtype: bool
2. Clean the Rating column and the other columns containing null values¶
In [69]:
df['Rating'].plot(kind = 'hist') #there are some rating above 5 starts which is incorrect we gonna deal with that
Out[69]:
<Axes: ylabel='Frequency'>
In [70]:
df.loc[df['Rating'] > 5 , 'Rating' ] = np.nan
In [71]:
df.loc[df['Rating'] > 5 ] #fantastic question 1 done
Out[71]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver |
|---|
In [72]:
df['Rating'].describe()
Out[72]:
count 9326.000000 mean 4.197727 std 0.508178 min 1.000000 25% 4.000000 50% 4.300000 75% 4.500000 max 5.000000 Name: Rating, dtype: float64
In [73]:
df['Rating'] = df['Rating'].fillna(df['Rating'].mean())
In [74]:
df['Rating'].isnull().sum() # question 2 done
Out[74]:
0
In [75]:
df.dropna(inplace = True) #question 3 done
In [76]:
df.shape
Out[76]:
(10829, 13)
3. Clean the column Reviews and make it numeric¶
In [77]:
df['Reviews'].dtypes
Out[77]:
dtype('O')
In [78]:
# let's try to parse the review column to numeric but with a little argument that instead of raising an error when it's not
#possible to parse to numeric , it's just assigned a NaN to it
#let's see where are the errors
df_reviews_errors = df.copy()
df_reviews_errors ['Reviews Errors'] = pd.to_numeric(df['Reviews'] , errors = 'coerce')
df_reviews_errors.loc[df_reviews_errors['Reviews Errors'].isna()]
Out[78]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | Reviews Errors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 72 | Android Auto - Maps, Media, Messaging & Voice | AUTO_AND_VEHICLES | 4.2 | 2M | 16M | 10,000,000+ | Free | 0 | Teen | Auto & Vehicles | 11-Jul-18 | Varies with device | 5.0 and up | NaN |
| 1778 | Block Craft 3D: Building Simulator Games For Free | GAME | 4.5 | 1M | 57M | 50,000,000+ | Free | 0 | Everyone | Simulation | 5-Mar-18 | 2.10.2 | 4.0.3 and up | NaN |
| 1781 | Trivia Crack | GAME | 4.5 | 6.4M | 95M | 100,000,000+ | Free | 0 | Everyone | Trivia | 3-Aug-18 | 2.79.0 | 4.1 and up | NaN |
In [79]:
M_reviews = pd.to_numeric(df.loc[df['Reviews'].str.contains('M') , 'Reviews'].str.replace('M' , '') ) * 1000000
K_reviews = pd.to_numeric(df.loc[df['Reviews'].str.contains('K') , 'Reviews'].str.replace('K' , '') ) * 1000
In [80]:
M_reviews
Out[80]:
72 2000000.0 1778 1000000.0 1781 6400000.0 Name: Reviews, dtype: float64
In [81]:
K_reviews
Out[81]:
Series([], Name: Reviews, dtype: int64)
In [82]:
df.loc[df['Reviews'].str.contains('M') , 'Reviews'] = M_reviews.astype(str)
In [83]:
df.iloc[71] #Perfect
Out[83]:
App Android Auto - Maps, Media, Messaging & Voice Category AUTO_AND_VEHICLES Rating 4.2 Reviews 2000000.0 Size 16M Installs 10,000,000+ Type Free Price 0 Content Rating Teen Genres Auto & Vehicles Last Updated 11-Jul-18 Current Ver Varies with device Android Ver 5.0 and up Name: 72, dtype: object
In [84]:
#now let's make the column numerical
df['Reviews'] = pd.to_numeric(df['Reviews'])
In [85]:
df['Reviews'].dtypes
Out[85]:
dtype('float64')
4. How many duplicated apps are there?¶
In [86]:
df['App'].duplicated().any()
Out[86]:
True
In [87]:
#quick view of duplicates
df.loc[df['App'].duplicated(keep =False)].sort_values('App').head() # sort values to make the duplicates appear next to each others
Out[87]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1393 | 10 Best Foods for You | HEALTH_AND_FITNESS | 4.0 | 2490.0 | 3.8M | 500,000+ | Free | 0 | Everyone 10+ | Health & Fitness | 17-Feb-17 | 1.9 | 2.3.3 and up |
| 1407 | 10 Best Foods for You | HEALTH_AND_FITNESS | 4.0 | 2490.0 | 3.8M | 500,000+ | Free | 0 | Everyone 10+ | Health & Fitness | 17-Feb-17 | 1.9 | 2.3.3 and up |
| 2543 | 1800 Contacts - Lens Store | MEDICAL | 4.7 | 23160.0 | 26M | 1,000,000+ | Free | 0 | Everyone | Medical | 27-Jul-18 | 7.4.1 | 5.0 and up |
| 2322 | 1800 Contacts - Lens Store | MEDICAL | 4.7 | 23160.0 | 26M | 1,000,000+ | Free | 0 | Everyone | Medical | 27-Jul-18 | 7.4.1 | 5.0 and up |
| 2385 | 2017 EMRA Antibiotic Guide | MEDICAL | 4.4 | 12.0 | 3.8M | 1,000+ | Paid | $16.99 | Everyone | Medical | 27-Jan-17 | 1.0.5 | 4.0.3 and up |
In [88]:
df['App'].duplicated(keep =False).sum()
#let's explain some things : by default pandas do the following :
# Twitter
# Twitter X (this is the duplicate)
# Facebook
# Facebook X
# Facebook X
# Facebook X
# Facebook X
#means there are 5 duplicates (takes the original and count the rest as duplicates)
#but in this activity it's asked to count duplicates with the original one , so we enter the argument keep = False
Out[88]:
1979
5. Drop duplicated apps keeping the ones with the greatest number of reviews¶
In [89]:
df = df.sort_values(['App' , 'Reviews'])
df = df.drop_duplicates(subset = ['App'], keep='last') #if ur wondering why i sorted items ascending and deleted the last item
#basically the activity checks it when the indexes are in this order , stupid yes !
In [90]:
df
Out[90]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8884 | "i DT" Fútbol. Todos Somos Técnicos. | SPORTS | 4.197727 | 27.0 | 3.6M | 500+ | Free | 0 | Everyone | Sports | 7-Oct-17 | 0.22 | 4.1 and up |
| 324 | #NAME? | COMICS | 3.500000 | 115.0 | 9.1M | 10,000+ | Free | 0 | Mature 17+ | Comics | 13-Jul-18 | 5.0.12 | 5.0 and up |
| 8532 | +Download 4 Instagram Twitter | SOCIAL | 4.500000 | 40467.0 | 22M | 1,000,000+ | Free | 0 | Everyone | Social | 2-Aug-18 | 5.03 | 4.1 and up |
| 4541 | .R | TOOLS | 4.500000 | 259.0 | 203k | 10,000+ | Free | 0 | Everyone | Tools | 16-Sep-14 | 1.1.06 | 1.5 and up |
| 4636 | /u/app | COMMUNICATION | 4.700000 | 573.0 | 53M | 10,000+ | Free | 0 | Mature 17+ | Communication | 3-Jul-18 | 4.2.4 | 4.1 and up |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6334 | 뽕티비 - 개인방송, 인터넷방송, BJ방송 | VIDEO_PLAYERS | 4.197727 | 414.0 | 59M | 100,000+ | Free | 0 | Mature 17+ | Video Players & Editors | 18-Jul-18 | 4.0.7 | 4.0.3 and up |
| 4362 | 💎 I'm rich | LIFESTYLE | 3.800000 | 718.0 | 26M | 10,000+ | Paid | $399.99 | Everyone | Lifestyle | 11-Mar-18 | 1.0.0 | 4.4 and up |
| 2575 | 💘 WhatsLov: Smileys of love, stickers and GIF | SOCIAL | 4.600000 | 22098.0 | 18M | 1,000,000+ | Free | 0 | Everyone | Social | 24-Jul-18 | 4.2.4 | 4.0.3 and up |
| 7559 | 📏 Smart Ruler ↔️ cm/inch measuring for homework! | TOOLS | 4.000000 | 19.0 | 3.2M | 10,000+ | Free | 0 | Everyone | Tools | 21-Oct-17 | 1 | 4.2 and up |
| 882 | 🔥 Football Wallpapers 4K | Full HD Backgrounds 😍 | ENTERTAINMENT | 4.700000 | 11661.0 | 4.0M | 1,000,000+ | Free | 0 | Everyone | Entertainment | 14-Jul-18 | 1.1.3.2 | 4.0.3 and up |
9648 rows × 13 columns
In [91]:
df.loc[df['App'].duplicated(keep =False)].sort_values('App').head()
Out[91]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver |
|---|
6. Format the Category column¶
In [92]:
df.head()
Out[92]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8884 | "i DT" Fútbol. Todos Somos Técnicos. | SPORTS | 4.197727 | 27.0 | 3.6M | 500+ | Free | 0 | Everyone | Sports | 7-Oct-17 | 0.22 | 4.1 and up |
| 324 | #NAME? | COMICS | 3.500000 | 115.0 | 9.1M | 10,000+ | Free | 0 | Mature 17+ | Comics | 13-Jul-18 | 5.0.12 | 5.0 and up |
| 8532 | +Download 4 Instagram Twitter | SOCIAL | 4.500000 | 40467.0 | 22M | 1,000,000+ | Free | 0 | Everyone | Social | 2-Aug-18 | 5.03 | 4.1 and up |
| 4541 | .R | TOOLS | 4.500000 | 259.0 | 203k | 10,000+ | Free | 0 | Everyone | Tools | 16-Sep-14 | 1.1.06 | 1.5 and up |
| 4636 | /u/app | COMMUNICATION | 4.700000 | 573.0 | 53M | 10,000+ | Free | 0 | Mature 17+ | Communication | 3-Jul-18 | 4.2.4 | 4.1 and up |
In [93]:
df['Category'].value_counts() #doesn't seem to be wrong values , everything's good !
Out[93]:
Category FAMILY 1874 GAME 945 TOOLS 827 BUSINESS 420 MEDICAL 395 PRODUCTIVITY 374 PERSONALIZATION 374 LIFESTYLE 369 FINANCE 345 SPORTS 325 COMMUNICATION 315 HEALTH_AND_FITNESS 288 PHOTOGRAPHY 281 NEWS_AND_MAGAZINES 254 SOCIAL 239 BOOKS_AND_REFERENCE 221 TRAVEL_AND_LOCAL 219 SHOPPING 202 DATING 170 VIDEO_PLAYERS 164 MAPS_AND_NAVIGATION 131 FOOD_AND_DRINK 112 EDUCATION 105 ENTERTAINMENT 86 AUTO_AND_VEHICLES 85 LIBRARIES_AND_DEMO 83 WEATHER 79 HOUSE_AND_HOME 73 EVENTS 64 ART_AND_DESIGN 60 PARENTING 60 COMICS 56 BEAUTY 53 Name: count, dtype: int64
In [94]:
df['Category'] = df['Category'].str.replace('_' , ' ' )
df['Category'] = df['Category'].apply(lambda x : x.lower().capitalize()) #lambda the best
In [95]:
df['Category'].value_counts()
Out[95]:
Category Family 1874 Game 945 Tools 827 Business 420 Medical 395 Productivity 374 Personalization 374 Lifestyle 369 Finance 345 Sports 325 Communication 315 Health and fitness 288 Photography 281 News and magazines 254 Social 239 Books and reference 221 Travel and local 219 Shopping 202 Dating 170 Video players 164 Maps and navigation 131 Food and drink 112 Education 105 Entertainment 86 Auto and vehicles 85 Libraries and demo 83 Weather 79 House and home 73 Events 64 Art and design 60 Parenting 60 Comics 56 Beauty 53 Name: count, dtype: int64
7. Clean and convert the Installs column to numeric type¶
In [96]:
df.sample(5)
Out[96]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2740 | Nordstrom | Shopping | 4.6 | 2278.0 | 12M | 500,000+ | Free | 0 | Everyone | Shopping | 15-Jul-18 | 3.28.1.3.1359 | 4.4 and up |
| 571 | Moco+ - Chat, Meet People | Dating | 4.2 | 1546.0 | Varies with device | 10,000+ | Paid | $3.99 | Mature 17+ | Dating | 19-Jun-18 | 2.6.139 | 4.1 and up |
| 10792 | Soccer Clubs Logo Quiz | Game | 4.2 | 21661.0 | 16M | 1,000,000+ | Free | 0 | Everyone | Trivia | 24-May-18 | 1.3.81 | 4.0 and up |
| 6477 | BM Pharmacy | Health and fitness | 4.9 | 42.0 | 18M | 1,000+ | Free | 0 | Everyone | Health & Fitness | 11-Jun-18 | 1.5.12 | 4.1 and up |
| 4428 | WiFi-o-Matic | Productivity | 4.0 | 736.0 | 6.8M | 50,000+ | Free | 0 | Everyone | Productivity | 1-Feb-18 | 1.80.03 | 4.1 and up |
In [97]:
df['Installs'] =df['Installs'].str.replace('[^0-9]' , '' ,regex=True)
df.sample(5)
Out[97]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7768 | Chest Simulator for Clash Royale | Family | 4.4 | 4756.0 | 33M | 100000 | Free | 0 | Everyone | Simulation | 19-Jul-18 | 1.1.9 | 4.1 and up |
| 4375 | Libre Scan (Diabetes:M addon) | Medical | 2.4 | 102.0 | 1.2M | 10000 | Free | 0 | Everyone | Medical | 9-Jul-17 | 1.2 | 4.4 and up |
| 10225 | Video Download For FB | Tools | 4.3 | 444.0 | 1.6M | 100000 | Free | 0 | Everyone | Tools | 14-Jul-17 | 1 | 3.0 and up |
| 5642 | Two Nights at jumpscare | Game | 3.8 | 596.0 | 91M | 100000 | Free | 0 | Teen | Adventure | 17-Aug-17 | 1 | 4.1 and up |
| 813 | Next Gen Science Standards | Education | 4.3 | 206.0 | 18M | 50000 | Free | 0 | Everyone | Education | 20-Dec-16 | 1.12.1 | 2.3 and up |
In [98]:
df['Installs'] = pd.to_numeric(df['Installs'])
In [99]:
df['Installs'].dtypes
Out[99]:
dtype('int64')
8. Clean and convert the Size column to numeric (representing bytes)¶
In [100]:
df.sample()
Out[100]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7551 | Inch/cm/Foot Conversion | Productivity | 4.0 | 319.0 | 2.1M | 100000 | Free | 0 | Everyone | Productivity | 20-Aug-17 | 2.0.1 | 2.3.3 and up |
In [101]:
df['Size'] =df['Size'].str.replace('[^a-zA-Z0-9.]' , '' ,regex=True)
In [102]:
M_size = pd.to_numeric(df.loc[df['Size'].str.contains('M') , 'Size'].str.replace('M' , '') ) * 1_048_576
K_size = pd.to_numeric(df.loc[df['Size'].str.contains('k') , 'Size'].str.replace('k' , '') ) * 1024
In [103]:
M_size.sample()
Out[103]:
3107 60817408.0 Name: Size, dtype: float64
In [104]:
K_size.sample()
Out[104]:
8218 110592.0 Name: Size, dtype: float64
In [105]:
df.loc[df['Size'].str.contains('M') , 'Size'] = M_size.astype(str)
df.loc[df['Size'].str.contains('k') , 'Size'] = K_size.astype(str)
In [106]:
df.loc[df['Size'].str.contains('[^0-9.]') , 'Size'].head()
Out[106]:
7338 Varieswithdevice 7330 Varieswithdevice 3448 Varieswithdevice 3151 Varieswithdevice 4875 Varieswithdevice Name: Size, dtype: object
In [107]:
df['Size'] = df['Size'].str.replace('Varieswithdevice' , '0')
In [108]:
df.loc[df['Size'].str.contains('[^0-9.]') , 'Size'].head()
Out[108]:
Series([], Name: Size, dtype: object)
In [109]:
df['Size'] = df['Size'].astype('float')
In [110]:
df['Size'].dtypes
Out[110]:
dtype('float64')
9. Clean and convert the Price column to numeric¶
In [111]:
df.loc[df['Type'] == 'Paid'].sample()
Out[111]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6132 | BG Monitor Diabetes Pro | Medical | 4.6 | 87.0 | 2936012.8 | 500 | Paid | $5.99 | Everyone | Medical | 23-Apr-17 | 8.0.1 | 4.4 and up |
In [112]:
df['Price'] = df['Price'].str.replace('$' , '')
df['Price'] = df['Price'].str.replace('Free' , '0')
In [113]:
df['Price'] = df['Price'].astype('float')
In [114]:
df.sample(5)
Out[114]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 465 | imo free video calls and chat | Communication | 4.300000 | 4785988.0 | 11534336.0 | 500000000 | Free | 0.00 | Everyone | Communication | 8-Jun-18 | 9.8.000000010501 | 4.0 and up |
| 9827 | ET Telecom from Economic Times | News and magazines | 4.700000 | 273.0 | 6606028.8 | 10000 | Free | 0.00 | Everyone | News & Magazines | 7-Aug-18 | 2.9.0 | 4.1 and up |
| 8571 | Interactive NPC DM Tool | Family | 4.197727 | 5.0 | 644096.0 | 50 | Paid | 0.99 | Everyone | Role Playing | 31-Jan-15 | 1.0.0 | 2.3.3 and up |
| 7085 | CA Mobile OTP | Tools | 3.300000 | 688.0 | 5767168.0 | 100000 | Free | 0.00 | Everyone | Tools | 5-Sep-17 | 2.3.5 | 2.3.3 and up |
| 1171 | Citi Mobile® | Finance | 4.000000 | 78306.0 | 48234496.0 | 5000000 | Free | 0.00 | Everyone | Finance | 31-Jul-18 | 9.9.0 | 4.4 and up |
10. Paid or free?¶
In [115]:
df['Distribution'] = df['Type'].copy()
#stupid question , could've done it with lambda which iterate every value and assign paid or free based on a if statement
In [116]:
df.sample()
Out[116]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | Distribution | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3241 | Moto Voice | Tools | 4.1 | 33216.0 | 0.0 | 10000000 | Free | 0.0 | Everyone | Tools | 5-Jun-18 | Varies with device | Varies with device | Free |
Analysis¶
11. Which app has the most reviews?¶
In [117]:
df.sort_values('Reviews' , ascending=False)
Out[117]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | Distribution | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2544 | Social | 4.100000 | 78158306.0 | 0.0 | 1000000000 | Free | 0.0 | Teen | Social | 3-Aug-18 | Varies with device | Varies with device | Free | |
| 381 | WhatsApp Messenger | Communication | 4.400000 | 69119316.0 | 0.0 | 1000000000 | Free | 0.0 | Everyone | Communication | 3-Aug-18 | Varies with device | Varies with device | Free |
| 2604 | Social | 4.500000 | 66577446.0 | 0.0 | 1000000000 | Free | 0.0 | Teen | Social | 31-Jul-18 | Varies with device | Varies with device | Free | |
| 382 | Messenger – Text and Video Chat for Free | Communication | 4.000000 | 56646578.0 | 0.0 | 1000000000 | Free | 0.0 | Everyone | Communication | 1-Aug-18 | Varies with device | Varies with device | Free |
| 1879 | Clash of Clans | Game | 4.600000 | 44893888.0 | 102760448.0 | 100000000 | Free | 0.0 | Everyone 10+ | Strategy | 15-Jul-18 | 10.322.16 | 4.1 and up | Free |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5229 | AJ+ Beta | News and magazines | 4.197727 | 0.0 | 0.0 | 1000 | Free | 0.0 | Everyone | News & Magazines | 4-May-17 | Varies with device | Varies with device | Free |
| 5255 | AJ and Alyssa | Lifestyle | 4.197727 | 0.0 | 486400.0 | 100 | Free | 0.0 | Everyone | Lifestyle | 1-Jun-17 | 1.0.0 | 2.3.3 and up | Free |
| 10692 | MARKET FO | Communication | 4.197727 | 0.0 | 15728640.0 | 100 | Free | 0.0 | Everyone | Communication | 30-Nov-17 | 1 | 4.1 and up | Free |
| 5270 | AJ Wallpapers | Personalization | 4.197727 | 0.0 | 4089446.4 | 100 | Free | 0.0 | Everyone | Personalization | 6-May-18 | 2 | 4.0.3 and up | Free |
| 9139 | DZ Register | Productivity | 4.197727 | 0.0 | 18874368.0 | 1 | Free | 0.0 | Everyone | Productivity | 31-Jul-18 | 1.0.6 | 4.1 and up | Free |
9648 rows × 14 columns
12. What category has the highest number of apps uploaded to the store?¶
In [119]:
df['Category'].value_counts().sort_values(ascending=False).head(1)
Out[119]:
Category Family 1874 Name: count, dtype: int64
13. To which category belongs the most expensive app?¶
In [125]:
df.sort_values('Price' , ascending=False )['Category'].head(1)
Out[125]:
4367 Lifestyle Name: Category, dtype: object
14. What's the name of the most expensive game?¶
In [126]:
df.loc[df['Category'] == 'Game'].sort_values('Price' , ascending=False )['App'].head(1)
Out[126]:
4203 The World Ends With You Name: App, dtype: object
15. Which is the most popular Finance App?¶
In [127]:
df.head()
Out[127]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | Distribution | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8884 | "i DT" Fútbol. Todos Somos Técnicos. | Sports | 4.197727 | 27.0 | 3774873.6 | 500 | Free | 0.0 | Everyone | Sports | 7-Oct-17 | 0.22 | 4.1 and up | Free |
| 324 | #NAME? | Comics | 3.500000 | 115.0 | 9542041.6 | 10000 | Free | 0.0 | Mature 17+ | Comics | 13-Jul-18 | 5.0.12 | 5.0 and up | Free |
| 8532 | +Download 4 Instagram Twitter | Social | 4.500000 | 40467.0 | 23068672.0 | 1000000 | Free | 0.0 | Everyone | Social | 2-Aug-18 | 5.03 | 4.1 and up | Free |
| 4541 | .R | Tools | 4.500000 | 259.0 | 207872.0 | 10000 | Free | 0.0 | Everyone | Tools | 16-Sep-14 | 1.1.06 | 1.5 and up | Free |
| 4636 | /u/app | Communication | 4.700000 | 573.0 | 55574528.0 | 10000 | Free | 0.0 | Mature 17+ | Communication | 3-Jul-18 | 4.2.4 | 4.1 and up | Free |
In [128]:
df.loc[df['Category'] == 'Finance'].sort_values('Installs' , ascending=False )['App'].head(1)
Out[128]:
5601 Google Pay Name: App, dtype: object
16. What Teen Game has the most reviews?¶
In [130]:
df.loc[(df['Category'] == 'Game') & (df['Content Rating'] == 'Teen')].sort_values('Reviews' , ascending=False )['App'].head(1)
Out[130]:
3912 Asphalt 8: Airborne Name: App, dtype: object
17. Which is the free game with the most reviews?¶
In [133]:
df.loc[(df['Category'] == 'Game') & (df['Distribution'] == 'Free')].sort_values('Reviews' , ascending=False )['App'].head(1)
Out[133]:
1879 Clash of Clans Name: App, dtype: object
18. How many Tb (tebibytes) were transferred (overall) for the most popular Lifestyle app?¶
In [135]:
df.loc[df['Category'] == 'Lifestyle'].sort_values('Installs' , ascending=False ).head(1)
Out[135]:
| App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | Distribution | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4587 | Tinder | Lifestyle | 4.0 | 2789775.0 | 71303168.0 | 100000000 | Free | 0.0 | Mature 17+ | Lifestyle | 2-Aug-18 | 9.5.0 | 4.4 and up | Free |
In [136]:
tinder_size = 71303168.0
tinder_installations = 100000000
In [138]:
(tinder_size * tinder_installations) / (2**40)
Out[138]:
6484.9853515625
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