EDA Google Play App Store

About Dataset

Description
The Processed DataSet can be downloaded from the following link
If you prefer to work with the original DataSet it can also be downloaded from the following link

Context
I fixed a lot of problems within the dataset in part 1 of this notebook. Now, you can download the fixed dataset and continue with part 2. Part 1 is done, and you can get the dataset from the link above. I’m sharing part 1 to help you understand what I did with the dataset. If you prefer to work with the original dataset, you can download it along with the processed one.

If you want to quickly run and test this notebook on Kaggle, just click on this link

Content
Each app (row) has values for App id, installs, category, rating, size, price, rating count, and more.

Acknowledgements
This data is scraped from the Google Play Store. Without it, we wouldn’t have this valuable app information. The data from the Play Store has huge potential to help app businesses succeed. Developers can use it to gain useful insights and conquer the Android market!

Part 1

Importing Libraries


import pandas as pd  # Data manipulation and analysis library
import numpy as np   # Numerical computing library

# Visualization Libraries
import matplotlib.pyplot as plt  # Data visualization library
import seaborn as sns            # Statistical data visualization library
%matplotlib inline

Data Loading and exploration and cleaning

Load the csv file with the pandas.
creating the dataframe and understanding the data present in the dataset using pandas.
Dealing with the missing data, outliers and the incorrect records.

# Load the Dataset
df = pd.read_csv('./Data/Google-Playstore.csv')

Viewing the first five Rows of the data

df.head().T

	0	1	2	3	4
App Name	Gakondo	Ampere Battery Info	Vibook	Smart City Trichy Public Service Vehicles 17UC…	GROW.me
App Id	com.ishakwe.gakondo	com.webserveis.batteryinfo	com.doantiepvien.crm	cst.stJoseph.ug17ucs548	com.horodyski.grower
Category	Adventure	Tools	Productivity	Communication	Tools
Rating	0.0	4.4	0.0	5.0	0.0
Rating Count	0.0	64.0	0.0	5.0	0.0
Installs	10+	5,000+	50+	10+	100+
Minimum Installs	10.0	5000.0	50.0	10.0	100.0
Maximum Installs	15	7662	58	19	478
Free	True	True	True	True	True
Price	0.0	0.0	0.0	0.0	0.0
Currency	USD	USD	USD	USD	USD
Size	10M	2.9M	3.7M	1.8M	6.2M
Minimum Android	7.1 and up	5.0 and up	4.0.3 and up	4.0.3 and up	4.1 and up
Developer Id	Jean Confident Irénée NIYIZIBYOSE	Webserveis	Cabin Crew	Climate Smart Tech2	Rafal Milek-Horodyski
Developer Website	https://beniyizibyose.tk/#/	https://webserveis.netlify.app/	NaN	http://www.climatesmarttech.com/	http://www.horodyski.com.pl
Developer Email	jean21101999@gmail.com	webserveis@gmail.com	vnacrewit@gmail.com	climatesmarttech2@gmail.com	rmilekhorodyski@gmail.com
Released	Feb 26, 2020	May 21, 2020	Aug 9, 2019	Sep 10, 2018	Feb 21, 2020
Last Updated	Feb 26, 2020	May 06, 2021	Aug 19, 2019	Oct 13, 2018	Nov 12, 2018
Content Rating	Everyone	Everyone	Everyone	Everyone	Everyone
Privacy Policy	https://beniyizibyose.tk/projects/	https://dev4phones.wordpress.com/licencia-de-uso/	https://www.vietnamairlines.com/vn/en/terms-an…	NaN	http://www.horodyski.com.pl
Ad Supported	False	True	False	True	False
In App Purchases	False	False	False	False	False
Editors Choice	False	False	False	False	False
Scraped Time	2021-06-15 20:19:35	2021-06-15 20:19:35	2021-06-15 20:19:35	2021-06-15 20:19:35	2021-06-15 20:19:35

Let’s see the exact column names

# Display the column names
df.columns

Index([‘App Name’, ‘App Id’, ‘Category’, ‘Rating’, ‘Rating Count’, ‘Installs’,
‘Minimum Installs’, ‘Maximum Installs’, ‘Free’, ‘Price’, ‘Currency’,
‘Size’, ‘Minimum Android’, ‘Developer Id’, ‘Developer Website’,
‘Developer Email’, ‘Released’, ‘Last Updated’, ‘Content Rating’,
‘Privacy Policy’, ‘Ad Supported’, ‘In App Purchases’, ‘Editors Choice’,
‘Scraped Time’],
dtype=’object’)

Let’s have a look on the shape of the dataset

print(f"The dataframe has {df.shape[0]} rows and {df.shape[1]} columns")

The dataframe has 2312944 rows and 24 columns

Let’s have a look on the columns and their data types using detailed info function

# Display detailed information about the dataset
df.info()

RangeIndex: 2312944 entries, 0 to 2312943
Data columns (total 24 columns):
# Column Dtype
— —— —–
0 App Name object
1 App Id object
2 Category object
3 Rating float64
4 Rating Count float64
5 Installs object
6 Minimum Installs float64
7 Maximum Installs int64
8 Free bool
9 Price float64
10 Currency object
11 Size object
12 Minimum Android object
13 Developer Id object
14 Developer Website object
15 Developer Email object
16 Released object
17 Last Updated object
18 Content Rating object
19 Privacy Policy object
20 Ad Supported bool
21 In App Purchases bool
22 Editors Choice bool
23 Scraped Time object
dtypes: bool(4), float64(4), int64(1), object(15)
memory usage: 361.8+ MB

Some Observations

There are 2312944 rows and 24 columns in the dataset

The columns are of different data types

The columns in the datasets are:

'App Name', 'App Id', 'Category', 'Rating', 'Rating Count', 'Installs', 'Minimum Installs', 'Maximum Installs', 'Free', 'Price', 'Currency', 'Size', 'Minimum Android' 'Developer Id', 'Developer Website', 'Developer Email', 'Released', 'Last Updated', 'Content Rating', 'Privacy Policy', 'Ad Supported', 'In App Purchases', 'Editors Choice', 'Scraped Time',

There are some missing values in the dataset which we will read in details and deal later on in the notebook.

Some columns are currently stored as object data type, but they should be numeric. We’ll convert them later in the notebook once we decide which columns to keep and which to drop i.e. ‘Size’.

Let’s display descriptive statistics for numerical columns

# Display descriptive statistics for numerical columns
df.describe()

	Rating	Rating Count	Minimum Installs	Maximum Installs	Price
count	2290061.000	2290061.000	2312837.000	2312944.000	2312944.000
mean	2.203	2864.839	183445.214	320201.713	0.103
std	2.106	212162.571	15131439.060	23554954.887	2.633
min	0.000	0.000	0.000	0.000	0.000
25%	0.000	0.000	50.000	84.000	0.000
50%	2.900	6.000	500.000	695.000	0.000
75%	4.300	42.000	5000.000	7354.000	0.000
max	5.000	138557570.000	10000000000.000	12057627016.000	400.000

If numeric values appear in scientific notation—a method to simplify the presentation of very large or very small numbers—you have the option to adjust settings to show these numbers in full. I favor viewing numbers in their entirety, without scientific notation. Additionally, a reset option is provided, allowing you to comment it out if you wish to return the display settings to their original state.

# Set pandas display options
pd.set_option('display.float_format', lambda x: '%.3f' % x)
# Reset pandas display options
# pd.reset_option('display.float_format')

Statistics Observations

Rating Diversity:
Ratings vary from 0 to 5, showcasing diverse user opinions on app quality.

User Engagement Range:
Rating Count’ spans from 0 to 138,557,600, indicating varying app popularity.

Installation Metrics Spectrum:
‘Minimum’ and ‘Maximum Installs’ reflect a wide range, from 0 to 5 billion, highlighting diverse app popularity.

Pricing Landscape:
Most apps are free (75% with a price of 0), but premium-priced apps exist, reaching a max of 399.99.

Numeric Considerations: ‘Size’ and ‘Installs’ columns may contain non-numeric characters (‘M’, ‘K’, ‘Varies with device’, ‘+’). Transforming them into numeric formats is crucial for accurate numerical operations.

Let’s clean the `Size` column first

In Statistics Observations we observed that ‘Size’ ‘ columns contain non-numeric characters (‘M’,’K’, ‘Varies with device’, ). So lets check size columns.

# Check values in 'Size' Column 
df['Size'].value_counts()

Varies with device 74777
11M 62157
12M 56080
13M 48034
14M 45211
…
8.7k 1
784M 1
385M 1
7.6k 1
512M 1
Name: Size, Length: 1657, dtype: int64

You can expand the viewing limit for columns and rows by utilizing the following optional commands.

pd.set_option('display.max_columns', None) # this is to display all the columns in the dataframe
pd.set_option('display.max_rows', None) # this is to display all the rows in the dataframe

Re-check value count in ‘Size’ Column

# Check values in 'Size' Column 
df['Size'].value_counts()

Varies with device 74777
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701k 29
155k 29
248k 29
203k 29
852k 29
189M 29
494k 29
636k 29
337k 29
592k 29
518k 29
627k 29
740k 29
272k 29
649k 29
586k 29
179k 29
719k 29
111k 29
548k 29
634k 29
395k 28
387k 28
560k 28
341k 28
457k 28
648k 28
423k 28
392k 28
535k 28
613k 28
818k 28
496k 28
794k 28
570k 28
640k 28
292k 28
550k 28
624k 28
545k 28
313k 28
700k 28
619k 28
381k 28
752k 28
638k 28
191M 28
705k 28
674k 28
318k 28
492k 28
299k 28
382k 28
671k 28
682k 28
125k 28
847k 28
324k 28
579k 28
374k 28
79k 28
467k 28
71k 27
783k 27
510k 27
598k 27
479k 27
720k 27
656k 27
481k 27
610k 27
426k 27
136k 27
400k 27
356k 27
115k 27
368k 27
375k 27
207k 27
574k 27
455k 27
422k 27
506k 27
500k 27
175M 27
346k 27
411k 27
473k 27
320k 27
195M 27
673k 27
192M 27
454k 27
697k 27
860k 27
408k 27
626k 27
401k 27
645k 27
628k 27
635k 27
351k 27
681k 27
848k 27
366k 27
602k 27
197M 26
140k 26
484k 26
845k 26
759k 26
585k 26
715k 26
551k 26
578k 26
432k 26
186M 26
534k 26
295k 26
710k 26
822k 26
536k 26
11k 26
402k 26
659k 26
530k 26
429k 26
680k 26
797k 26
686k 26
190M 26
425k 26
116k 26
363k 26
600k 26
653k 26
809k 26
471k 26
206M 26
294k 26
595k 25
480k 25
582k 25
1,010k 25
571k 25
403k 25
418k 25
695k 25
355k 25
517k 25
288k 25
949k 25
340k 25
424k 25
483k 25
639k 25
663k 25
771k 25
670k 25
615k 25
507k 25
260k 25
121k 25
971k 25
803k 25
521k 24
567k 24
404k 24
336k 24
563k 24
584k 24
601k 24
264k 24
698k 24
561k 24
781k 24
539k 24
398k 24
533k 24
614k 24
112k 24
658k 24
379k 24
758k 24
606k 24
120k 24
501k 24
416k 24
489k 24
660k 24
661k 24
731k 24
587k 24
541k 24
339k 24
664k 24
415k 24
553k 24
580k 24
450k 24
435k 23
131k 23
665k 23
581k 23
544k 23
593k 23
476k 23
765k 23
557k 23
384k 23
540k 23
458k 23
605k 23
519k 23
344k 23
123k 23
325k 23
655k 23
728k 23
427k 23
335k 23
572k 23
723k 23
144k 23
751k 23
1,013k 23
307k 23
609k 22
569k 22
689k 22
135k 22
617k 22
498k 22
537k 22
304k 22
459k 22
443k 22
792k 22
607k 22
591k 22
596k 22
909k 22
669k 22
547k 22
461k 22
604k 22
376k 22
528k 21
512k 21
472k 21
515k 21
520k 21
603k 21
470k 21
608k 21
311k 21
801k 21
499k 21
203M 21
104k 21
442k 21
502k 21
532k 21
207M 21
549k 21
488k 20
590k 20
662k 20
447k 20
185M 20
444k 20
508k 20
732k 20
555k 20
650k 20
565k 20
583k 20
468k 20
637k 20
630k 19
451k 19
556k 19
380k 19
352k 19
119k 19
482k 19
559k 19
611k 19
764k 19
684k 19
564k 19
576k 19
372k 18
235M 18
524k 18
456k 18
513k 18
709k 18
216M 18
215M 18
464k 18
632k 17
657k 17
205M 17
345k 17
1,024k 17
199M 17
218M 17
296k 17
204M 17
668k 17
200M 17
349k 17
10k 16
211M 15
214M 15
327k 15
213M 15
233M 14
756k 14
230M 13
255M 13
227M 12
210M 12
228M 12
242M 12
250M 11
256M 11
226M 11
208M 11
231M 11
243M 10
224M 10
237M 10
223M 10
220M 10
241M 9
244M 9
217M 9
234M 9
219M 9
238M 9
232M 9
287M 9
257M 8
249M 8
280M 8
1.1G 8
248M 8
565M 8
222M 8
221M 8
270M 8
225M 7
317M 7
246M 7
264M 7
253M 7
261M 7
245M 7
276M 7
332M 6
299M 6
236M 6
240M 6
321M 6
247M 6
229M 6
319M 6
262M 6
252M 5
251M 5
281M 5
298M 5
309M 5
239M 5
8.4k 5
6.8k 5
297M 5
6.4k 5
263M 5
258M 5
330M 4
300M 4
289M 4
8.9k 4
9.1k 4
293M 4
277M 4
310M 4
306M 4
355M 4
260M 4
269M 4
327M 4
9.6k 4
304M 4
339M 4
335M 4
567M 4
267M 4
338M 4
322M 4
285M 4
274M 4
303M 4
391M 3
448M 3
266M 3
390M 3
9.8k 3
9.7k 3
9.9k 3
272M 3
1.0G 3
445M 3
344M 3
286M 3
331M 3
353M 3
323M 3
315M 3
283M 3
8.5k 3
375M 3
291M 3
382M 3
422M 3
265M 3
275M 3
290M 3
360M 3
313M 3
4.7k 3
268M 3
314M 3
254M 3
278M 2
359M 2
6.1k 2
398M 2
656M 2
329M 2
7.8k 2
279M 2
354M 2
454M 2
564M 2
284M 2
292M 2
408M 2
6.3k 2
508M 2
843M 2
343M 2
460M 2
333M 2
618M 2
311M 2
9.5k 2
9.3k 2
405M 2
372M 2
340M 2
7.1k 2
288M 2
259M 2
745M 2
1.5G 2
9.0k 2
9.2k 2
377M 2
387M 2
295M 2
348M 2
294M 2
296M 2
510M 2
324M 2
273M 2
352M 2
337M 2
305M 2
409M 2
334M 2
406M 2
366M 2
566M 2
467M 2
369M 1
465M 1
396M 1
896M 1
468M 1
810M 1
7.7k 1
570M 1
404M 1
415M 1
705M 1
302M 1
373M 1
590M 1
646M 1
839M 1
397M 1
869M 1
769M 1
301M 1
488M 1
692M 1
611M 1
593M 1
527M 1
424M 1
503M 1
866M 1
440M 1
461M 1
365M 1
431M 1
349M 1
581M 1
962M 1
470M 1
541M 1
799M 1
633M 1
5.1k 1
910M 1
679M 1
342M 1
370M 1
532M 1
3.2k 1
725M 1
442M 1
737M 1
691M 1
914M 1
394M 1
700M 1
356M 1
712M 1
5.3k 1
568M 1
830M 1
519M 1
8.3k 1
580M 1
977M 1
959M 1
981M 1
526M 1
379M 1
623M 1
533M 1
619M 1
521M 1
429M 1
744M 1
844M 1
889M 1
720M 1
6.2k 1
996M 1
371M 1
282M 1
437M 1
447M 1
497M 1
643M 1
456M 1
493M 1
414M 1
925M 1
645M 1
652M 1
420M 1
383M 1
312M 1
919M 1
904M 1
595M 1
485M 1
900M 1
361M 1
345M 1
7.2k 1
895M 1
954M 1
490M 1
3.4k 1
603M 1
1,006M 1
706M 1
509M 1
928M 1
320M 1
3.3k 1
4.6k 1
531M 1
364M 1
722M 1
501M 1
985M 1
412M 1
1,020M 1
841M 1
576M 1
690M 1
832M 1
10.0k 1
328M 1
818M 1
953M 1
6.9k 1
544M 1
426M 1
681M 1
347M 1
5.8k 1
606M 1
940M 1
271M 1
765M 1
664M 1
649M 1
411M 1
680M 1
514M 1
639M 1
963M 1
421M 1
457M 1
381M 1
318M 1
878M 1
351M 1
578M 1
868M 1
495M 1
935M 1
363M 1
518M 1
762M 1
491M 1
676M 1
684M 1
579M 1
550M 1
407M 1
591M 1
418M 1
7.4k 1
346M 1
8.7k 1
784M 1
385M 1
7.6k 1
512M 1
Name: Size, dtype: int64

We have a really big list to show, so we need to be careful about how much computer power we use when we show results, because our dataset is pretty big.

There are several unique values in the `size` column, we have to first make the unit into one common unit from M and K to bytes, and then remove the `M` and `K` from the values and convert them into numeric data type.

Let’s check how many values are in megabyte ‘M’ in it

# find the values in size column which has 'M' in it
df['Size'].loc[df['Size'].str.contains('M')].value_counts().sum()

{
“name”: “ValueError”,
“message”: “Cannot mask with non-boolean array containing NA / NaN values”,
“stack”: “—————————————————————————
ValueError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_10400\1502233449.py in
1 # find the values in size column which has ‘M’ in it
—-> 2 df[‘Size’].loc[df[‘Size’].str.contains(‘M’)].value_counts().sum()

c:\Users\ak\AppData\Local\miniconda3\envs\python_eda\lib\site-packages\pandas\core\indexing.py in getitem(self, key)
929
930 maybe_callable = com.apply_if_callable(key, self.obj)
–> 931 return self._getitem_axis(maybe_callable, axis=axis)
932
933 def _is_scalar_access(self, key: tuple):

c:\Users\ak\AppData\Local\miniconda3\envs\python_eda\lib\site-packages\pandas\core\indexing.py in _getitem_axis(self, key, axis)
1141 self._validate_key(key, axis)
1142 return self._get_slice_axis(key, axis=axis)
-> 1143 elif com.is_bool_indexer(key):
1144 return self._getbool_axis(key, axis=axis)
1145 elif is_list_like_indexer(key):

c:\Users\ak\AppData\Local\miniconda3\envs\python_eda\lib\site-packages\pandas\core\common.py in is_bool_indexer(key)
137 # Don’t raise on e.g. [\”A\”, \”B\”, np.nan], see
138 # test_loc_getitem_list_of_labels_categoricalindex_with_na
–> 139 raise ValueError(na_msg)
140 return False
141 return True

ValueError: Cannot mask with non-boolean array containing NA / NaN values”
}

We’re getting an error because there are some missing values in our data. So, first, we need to fix these missing values before we can work on the ‘size’ column.

# Code to check and display the count of missing values in each column, sorted in descending order
df.isnull().sum().sort_values(ascending=False)

Developer Website 760835
Privacy Policy 420953
Released 71053
Rating 22883
Rating Count 22883
Minimum Android 6530
Size 196
Currency 135
Installs 107
Minimum Installs 107
Developer Id 33
Developer Email 31
App Name 2
App Id 0
Price 0
Free 0
Maximum Installs 0
Last Updated 0
Content Rating 0
Category 0
Ad Supported 0
In App Purchases 0
Editors Choice 0
Scraped Time 0
dtype: int64

We can visualize missing values through heatmap.

# Create a heatmap to visualize missing values
plt.rcParams['figure.figsize'] = (15,6)
sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis')
plt.title("Distribution of Missing Values")
plt.show()

Let’s check what percentage of our data is missing.

#df.isnull().sum()/len(df)*100
missing_percentage = (df.isnull().sum().sort_values(ascending = False)/len(df))*100
missing_percentage

Developer Website 32.894657
Privacy Policy 18.199879
Released 3.071972
Rating 0.989345
Rating Count 0.989345
Minimum Android 0.282324
Size 0.008474
Currency 0.005837
Installs 0.004626
Minimum Installs 0.004626
Developer Id 0.001427
Developer Email 0.001340
App Name 0.000086
App Id 0.000000
Price 0.000000
Free 0.000000
Maximum Installs 0.000000
Last Updated 0.000000
Content Rating 0.000000
Category 0.000000
Ad Supported 0.000000
In App Purchases 0.000000
Editors Choice 0.000000
Scraped Time 0.000000
dtype: float64

Dealing with the null values

Developer Website has the highest percentage of null values (32.89%).
Privacy Policy shows significant null values (18.19%).
Released has approximately 3.07% null values.
Rating and Rating Count both have 0.98% null values.
Minimum Android has 0.28% null values.
Currency have low null values (around 0.005%).
Installs, Minimum Installs, and Currency have very low null values (around 0.004%).
Remaining features (Developer Email, Developer Id, Size, App Name) have extremely low null values (less than 0.001%).

To make things simpler and less sentimental, I’ve chosen to delete the following columns: ‘Developer Website’, ‘Privacy Policy’, ‘Developer Email’, ‘In App Purchases’, ‘Editors Choice’, ‘Ad Supported’, and ‘Scraped Time’.

# Columns to remove
columns_to_remove = ['Developer Website', 'Privacy Policy', 'Developer Email',
                      'In App Purchases', 'Editors Choice', 'Ad Supported', 'Scraped Time']

# Drop the specified columns in-place
df.drop(columns=columns_to_remove, axis=1, inplace=True)

df.head()

	App Name	App Id	Category	Rating	Rating Count	Installs	Minimum Installs	Maximum Installs	Free	Currency	Size	Minimum Android	Developer Id	Released	Last Updated	Content Rating
0	Gakondo	com.ishakwe.gakondo	Adventure	0.0	0.0	10+	10.0	15	True	USD	10M	7.1 and up	Jean Confident Irénée NIYIZIBYOSE	Feb 26, 2020	Feb 26, 2020	Everyone
1	Ampere Battery Info	com.webserveis.batteryinfo	Tools	4.4	64.0	5,000+	5000.0	7662	True	USD	2.9M	5.0 and up	Webserveis	May 21, 2020	May 06, 2021	Everyone
2	Vibook	com.doantiepvien.crm	Productivity	0.0	0.0	50+	50.0	58	True	USD	3.7M	4.0.3 and up	Cabin Crew	Aug 9, 2019	Aug 19, 2019	Everyone
3	Smart City Trichy Public Service Vehicles 17UC…	cst.stJoseph.ug17ucs548	Communication	5.0	5.0	10+	10.0	19	True	USD	1.8M	4.0.3 and up	Climate Smart Tech2	Sep 10, 2018	Oct 13, 2018	Everyone
4	GROW.me	com.horodyski.grower	Tools	0.0	0.0	100+	100.0	478	True	USD	6.2M	4.1 and up	Rafal Milek-Horodyski	Feb 21, 2020	Nov 12, 2018	Everyone

Imputation in Released Column

For the ‘Released’ column, which is crucial for app release dates, we’ve decided to fill in any missing values using the mode.
Using the mode, which is the most common date, is a good option for filling in missing values in categorical date data.
This ensures that we fill in missing values with the most common release date, helping to keep our data complete.

# Impute missing values in 'Released' column with the mode
released_mode = df['Released'].mode()[0]
df['Released'].fillna(released_mode, inplace=True)

To handle missing values in ‘Rating’ and ‘Rating Count,’ we opt for the median because it accurately reflects the middle value, aligning with the existing data pattern. This approach helps balance out extreme values, ensuring trustworthy outcomes.

# Impute missing values in 'Rating' and 'Rating Count' columns with the median
df['Rating'].fillna(df['Rating'].median(), inplace=True)
df['Rating Count'].fillna(df['Rating Count'].median(), inplace=True)

Now, let’s check for missing values again to make sure we’ve handled them properly.

df.isnull().sum().sort_values(ascending=False)

Minimum Android 6530
Size 196
Currency 135
Installs 107
Minimum Installs 107
Developer Id 33
App Name 2
Last Updated 0
Released 0
Free 0
Price 0
App Id 0
Maximum Installs 0
Rating Count 0
Rating 0
Category 0
Content Rating 0
dtype: int64

To handle missing values in ‘Installs’ and ‘Minimum Installs’ we opt for the median because it accurately reflects the middle value, aligning with the existing data pattern. This approach helps balance out extreme values, ensuring trustworthy outcomes.

# Check the data type of 'Minimum Installs' and convert if needed
if df['Minimum Installs'].dtype == 'object':
    df['Minimum Installs'] = df['Minimum Installs'].str.replace(',', '').str.extract('(\d+)').astype(float)

# Impute missing values with median
df['Minimum Installs'].fillna(df['Minimum Installs'].median(), inplace=True)

# Convert categorical values to numeric for 'Installs' column if it's in object format
if df['Installs'].dtype == 'object':
    df['Installs'] = df['Installs'].str.replace(',', '').str.extract('(\d+)').astype(float)

# Impute missing values with median
df['Installs'].fillna(df['Installs'].median(), inplace=True)

Now, let’s check for missing values again

df.isnull().sum().sort_values(ascending=False)

Minimum Android 6530
Size 196
Currency 135
Developer Id 33
App Name 2
Price 0
Last Updated 0
Released 0
Free 0
App Id 0
Maximum Installs 0
Minimum Installs 0
Installs 0
Rating Count 0
Rating 0
Category 0
Content Rating 0
dtype: int64

Drop Missing Values in Remaining Columns

Removing missing values guarantees that our data is complete, reliable, and accurate for analysis. This process ensures that important columns have all the necessary information, reduces the chance of biased results, and provides dependable data for making informed decisions.

# Drop all missing values in specific columns
# df.dropna(subset=['Minimum Android', 'Size', 'Currency', 'App Name','Developer Id'], inplace=True)
df.dropna(subset=['Minimum Android', 'Size', 'Currency', 'Developer Id', 'App Name'], inplace=True)

Let’s check if we have any missing values remaining

df.isnull().sum().sort_values(ascending=False)

App Name 0
Price 0
Last Updated 0
Released 0
Developer Id 0
Minimum Android 0
Size 0
Currency 0
Free 0
App Id 0
Maximum Installs 0
Minimum Installs 0
Installs 0
Rating Count 0
Rating 0
Category 0
Content Rating 0
dtype: int64

Because we successfully imputed all missing values so now we once again go to ‘size’ column and check how many values are in megabyte ‘M’ in it

# find the values in size column which has 'M' in it
df['Size'].loc[df['Size'].str.contains('M')].value_counts().sum()

2195318

# find the values in size column which has 'k' in it
df['Size'].loc[df['Size'].str.contains('k')].value_counts().sum()

36130

# find the values in size column which has 'Varies with device' in it
df['Size'].loc[df['Size'].str.contains('Varies with device')].value_counts().sum()

74588

# find the values in size column which has 'Varies with device' in it
df['Size'].loc[df['Size'].str.contains('G')].value_counts().sum()

We have 2195318 values in ‘M’ units
We have 36130 values in ‘k’ units
We have 74588 value in Varies with device
We have 13 values in ‘G’ units
Let’s convert the ‘G’ , ‘M’ and ‘k’ units into bytes and then remove the ‘G’ , ‘M’ and ‘k’ from the values and convert them into numeric data type.

# convert the size column to numeric by multiplying the values with 1024 if it has 'k' in it and 1024*1024 if it has 'M' in it
# this function will convert the size column to numeric
def convert_size(size):
    # add function details here
    '''
    This function will convert the size column to numeric by multiplying the values with 1024 if it has 'k' in it and 1024*1024 if it has 'M' in it
    '''
    
    if isinstance(size, str):
        # Remove commas from the string
        size = size.replace(',', '')
        if 'k' in size:
            return float(size.replace('k', '')) * 1024
        elif 'M' in size:
            return float(size.replace('M', '')) * 1024 * 1024
        elif 'G' in size:
            return float(size.replace('G', '')) * 1024 * 1024 * 1024
        elif 'Varies with device' in size:
            return np.nan
    return size

df['Size'] = df['Size'].apply(convert_size)

# rename the column name 'Size' to 'Size_in_bytes'
df.rename(columns={'Size': 'Size_in_bytes'}, inplace=True)

Let’s replace the missing values of ‘Varies with device’ with 0.

df['Size_in_bytes'].fillna(0, inplace=True)

Let’s display current info of Data Frame

df.info()

Int64Index: 2306049 entries, 0 to 2312943
Data columns (total 17 columns):
# Column Dtype
— —— —–
0 App Name object
1 App Id object
2 Category object
3 Rating float64
4 Rating Count float64
5 Installs float64
6 Minimum Installs float64
7 Maximum Installs int64
8 Free bool
9 Price float64
10 Currency object
11 Size_in_bytes float64
12 Minimum Android object
13 Developer Id object
14 Released object
15 Last Updated object
16 Content Rating object
dtypes: bool(1), float64(6), int64(1), object(9)
memory usage: 301.3+ MB

Let’s make a new column called ‘Size in Mb’ which will have the size in MB

# making a new column called 'Size in Mb' which will have the size in MB
df['Size_in_Mb'] = df['Size_in_bytes'].apply(lambda x: x/(1024*1024))

df.head()

	App Name	App Id	Category	Rating	Rating Count	Installs	Minimum Installs	Maximum Installs	Free	Currency	Size_in_bytes	Minimum Android	Developer Id	Released	Last Updated	Content Rating	Size_in_Mb
0	Gakondo	com.ishakwe.gakondo	Adventure	0.000	0.000	10.000	10.000	15	True	USD	10485760.000	7.1 and up	Jean Confident Irénée NIYIZIBYOSE	Feb 26, 2020	Feb 26, 2020	Everyone	10.000
1	Ampere Battery Info	com.webserveis.batteryinfo	Tools	4.400	64.000	5000.000	5000.000	7662	True	USD	3040870.400	5.0 and up	Webserveis	May 21, 2020	May 06, 2021	Everyone	2.900
2	Vibook	com.doantiepvien.crm	Productivity	0.000	0.000	50.000	50.000	58	True	USD	3879731.200	4.0.3 and up	Cabin Crew	Aug 9, 2019	Aug 19, 2019	Everyone	3.700
3	Smart City Trichy Public Service Vehicles 17UC…	cst.stJoseph.ug17ucs548	Communication	5.000	5.000	10.000	10.000	19	True	USD	1887436.800	4.0.3 and up	Climate Smart Tech2	Sep 10, 2018	Oct 13, 2018	Everyone	1.800
4	GROW.me	com.horodyski.grower	Tools	0.000	0.000	100.000	100.000	478	True	USD	6501171.200	4.1 and up	Rafal Milek-Horodyski	Feb 21, 2020	Nov 12, 2018	Everyone	6.200

df.columns

Index([‘App Name’, ‘App Id’, ‘Category’, ‘Rating’, ‘Rating Count’, ‘Installs’,
‘Minimum Installs’, ‘Maximum Installs’, ‘Free’, ‘Price’, ‘Currency’,
‘Size_in_bytes’, ‘Minimum Android’, ‘Developer Id’, ‘Released’,
‘Last Updated’, ‘Content Rating’, ‘Size_in_Mb’],
dtype=’object’)

Now that our processed dataset is free of missing values, you can skip part 1 of this notebook and proceed directly with the cleaned dataset of Google Play Store apps. You can download this dataset from the link.

Part 2

Import Libraries

import pandas as pd  # Data manipulation and analysis library
import numpy as np   # Numerical computing library

# Visualization Libraries
import matplotlib.pyplot as plt  # Data visualization library
import seaborn as sns            # Statistical data visualization library
%matplotlib inline

# Load the Dataset
df = pd.read_csv('./Data/google_play_store_cleaned.csv')

Now, we’re ready to explore various questions using the dataset.

Q1. How many apps in this dataset have duplicate names?
Q2. Please show number count of App Names
Q3. Please display Apps having name Age Calculator
Q4. How many different app prices are there in this dataset?
Q5. How many free apps are there in this dataset?
Q6. How many paid apps are there in this dataset?
Q7. What is the predominant content rating for most apps?
Q8. Please show me 10 sample apps data?
Q9. Does this dataset contain any duplicate rows?
Q10. Please display the top 20 app categories ranked by their installation counts.?
Q11. Please present the top 10 app categories with the highest average ratings?
Q12. Please display the average price of apps across different categories?
Q13. Please show me the top 10 app developers along with the number of apps they have developed?
Q14. Which Android version is most prevalent among top-rated apps?
Q15. Please display the top 10 most common Android versions among top-rated apps?
Q16. Please show me the top 10 most installed apps across all categories?
Q17. Please show me the top 5 highest-rated paid apps along with their ratings and prices?
Q18. Please show me the top 5 highest-rated free apps along with their ratings?
Q19. Please provide the count of apps in each category.
Q20. Determine the category with the highest prices among paid apps.
Q21. Please display the highest-priced apps within each category.
Q22. Please show me the top 10 rated apps in this dataset with the maximum number of user ratings.
Q23. Let’s find out the number of users for each rating to understand how many apps received ratings
Q24. Please display the year-on-year comparison of apps per content rating?
Q25. Please show me the number of apps released each year.
Q26. Please present a scatter plot to illustrate any relationship between user ratings and app prices?
Q27. Please create a scatter plot to visualize if there is any relationship between rating count and app size?
Q28. Show me top 5 Puzzle apps with highest ratings.
Q29. Show me top 5 Medical apps with highest ratings.
Q30. Please generate a bar plot showing the year-on-year breakdown of the top 5 categories based on app prices?

Q1. How many apps in this dataset have duplicate names?

# Find the duplicate in 'App Name' column
duplicate_app_names = df['App Name'].duplicated().sum()

print(f"There are total {duplicate_app_names} apps in dataset with duplicate names")

There are total 134549 apps in dataset with duplicate names

Q2. Please show number count of App Names

df['App Name'].value_counts().sort_values(ascending=False)

Tic Tac Toe 382
Calculator 260
Flashlight 256
BMI Calculator 199
Age Calculator 190
…
Trident Suvidha-Sales 1
My Office Solution 1
Dominoes BIG 1
Telesistema 1
Biliyor Musun – Sonsuz Yarış 1
Name: App Name, Length: 2171500, dtype: int64

Q3. Please display Apps having name Age Calculator

df[df['App Name'] == 'Age Calculator']

	App Name	App Id	Category	Rating	Rating Count	Installs	Minimum Installs	Maximum Installs	Free	Price	Currency	Size_in_bytes	Minimum Android	Developer Id	Released	Last Updated	Content Rating	Size_in_Mb
3337	Age Calculator	com.jawad.agecalculator	Tools	4.9	83.0	500.0	500.0	536	True	0.00	USD	5347737.6	4.4 and up	Mobix Tech	May 12, 2020	Jun 18, 2020	Everyone	5.100000
4630	Age Calculator	com.andywebsoft.agecalculator	Productivity	4.5	53.0	10000.0	10000.0	11228	True	0.00	USD	644096.0	2.3 and up	NSTechFrame	Jan 2, 2015	Feb 12, 2015	Everyone	0.614258
23583	Age Calculator	com.age46.agecalculator	Tools	4.7	22.0	1000.0	1000.0	1795	True	0.00	USD	3460300.8	2.3.3 and up	PureSoft	Feb 28, 2015	Mar 06, 2015	Everyone	3.300000
37176	Age Calculator	com.zakasoft.myage	Tools	4.1	9.0	1000.0	1000.0	2311	True	0.00	USD	4718592.0	4.4 and up	Zakaria Bin Abdur Rouf	Sep 26, 2017	Dec 30, 2020	Everyone	4.500000
40793	Age Calculator	com.codedonor.agecalculator	Personalization	0.0	0.0	1000.0	1000.0	1431	True	0.00	USD	1258291.2	4.0.3 and up	Proud Indian Studio	Jun 12, 2016	Jun 12, 2016	Everyone	1.200000
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
2218411	Age Calculator	com.ab.agecalculator	Tools	0.0	0.0	50.0	50.0	63	True	0.00	USD	2621440.0	5.0 and up	Arjun Bhatt	Dec 14, 2019	Dec 17, 2019	Everyone	2.500000
2223414	Age Calculator	com.lightofray.agecalculator	Tools	5.0	8.0	100.0	100.0	359	True	0.00	USD	3460300.8	5.0 and up	Light Of Rays	May 10, 2020	Dec 26, 2020	Everyone	3.300000
2279447	Age Calculator	com.kaushaldalvi.agecalcadfree	Tools	4.4	8.0	100.0	100.0	175	False	0.99	USD	3145728.0	2.3.3 and up	Kaushal Dalvi	Feb 25, 2011	Feb 11, 2014	Everyone	3.000000
2284793	Age Calculator	com.rudranetra.agecalculator	Tools	0.0	0.0	10.0	10.0	29	True	0.00	USD	4194304.0	5.0 and up	Rudra Netra	Nov 26, 2020	Dec 01, 2020	Everyone	4.000000
2296223	Age Calculator	in.accountmaster.agecalculator	Entertainment	4.4	123.0	5000.0	5000.0	9580	True	0.00	USD	3250585.6	4.0.3 and up	www.confodeal.com	Mar 17, 2016	Sep 28, 2016	Everyone	3.100000

190 rows × 18 columns

Q4. How many different app prices are there in this dataset?

diff_app_prices = df['Price'].nunique()

print(f"There are total {diff_app_prices} different prices of apps")

There are total 1061 different prices of apps

Q5. How many free apps are there in this dataset?

total_free_apps = df['Free'].value_counts()[True]

print(f"There are total {total_free_apps} free apps in this dataset")

There are total 2261395 free apps in this dataset

Q6. How many paid apps are there in this dataset?

total_paid_apps = df['Free'].value_counts()[False]

print(f"There are total {total_paid_apps} paid apps in this dataset")

There are total 44654 paid apps in this dataset

Q7. What is the predominant content rating for most apps?

df['Content Rating'].value_counts()

Everyone 2015931
Teen 195942
Mature 17+ 60127
Everyone 10+ 33761
Unrated 154
Adults only 18+ 134
Name: Content Rating, dtype: int64

Q8. Please show me 10 sample apps data?

df.sample(10)

	App Name	App Id	Category	Rating	Rating Count	Installs	Minimum Installs	Maximum Installs	Free	Currency	Size_in_bytes	Minimum Android	Developer Id	Released	Last Updated	Content Rating	Size_in_Mb
1735032	Radios Mexico	com.redeliteapps.radiosmexico	Music & Audio	0.0	0.0	100.0	100.0	359	True	USD	26214400.0	4.0.3 and up	Redelite apps	Oct 14, 2019	Oct 14, 2019	Everyone	25.0
379071	GetRunner Runner – Earn Extra Income Easily	com.getrunner.driver.application	Productivity	4.6	9.0	100.0	100.0	480	True	USD	12582912.0	5.0 and up	GetMove Sdn. Bhd.	Jun 17, 2020	Jun 02, 2021	Everyone	12.0
74353	The ResilientMind	com.myoutcomes.resilentmind	Health & Fitness	0.0	0.0	100.0	100.0	110	True	USD	34603008.0	4.2 and up	MyOutcomes For Mental Well Being Inc.	Jun 26, 2020	May 26, 2021	Everyone	33.0
708217	Pak Independence Photo Frames	com.happy.independence.day.photo.frames	Photography	4.7	757.0	100000.0	100000.0	466187	True	USD	11534336.0	4.0.3 and up	finkyfour	Jul 30, 2016	Feb 20, 2020	Everyone	11.0
1840075	BICC 2020	org.oncologyclub.bicc2020	Events	0.0	0.0	10.0	10.0	10	True	USD	3670016.0	4.4 and up	Shahadat Rigun	Feb 14, 2020	Feb 14, 2020	Everyone	3.5
897130	Mitsubishi TV Remote	com.tvremoteapp.mitsubishitvremote	Tools	3.6	65.0	10000.0	10000.0	12914	True	USD	14680064.0	4.4 and up	Just Remote Control	Mar 8, 2020	Mar 27, 2021	Everyone	14.0
1943772	The Urban Chic Boutique	com.shoptheurbanchic	Shopping	5.0	9.0	10.0	10.0	30	True	USD	39845888.0	5.0 and up	Rapid Acceleration Partners	Apr 11, 2021	Apr 11, 2021	Everyone	38.0
1076004	Lumbung Budaya Jogja	org.btkpdiy.lumbungbudayajogja	Education	4.3	36.0	1000.0	1000.0	1864	True	USD	8178892.8	4.1 and up	Balai Tekkomdik Dinas Dikpora DIY	Sep 9, 2015	Sep 10, 2015	Everyone	7.8
290693	German Operation Luttich 1944 (turn-limit)	com.cloudworth.falaiseg_free	Strategy	4.1	13.0	1000.0	1000.0	1251	True	USD	1258291.2	4.1 and up	Joni Nuutinen	Jul 12, 2019	May 05, 2021	Everyone 10+	1.2
1662068	Hair Style Salon Photo Editor	com.VAD.Hair.Style.Salon.Photo.Editor	Photography	2.7	1101.0	500000.0	500000.0	688789	True	USD	16777216.0	4.4 and up	Virtual Art Design	Jul 11, 2016	Apr 24, 2019	Everyone	16.0

Q9. Does this dataset contain any duplicate rows?

# Find duplicates in the data
dup_rows_in_df = df.duplicated().sum()

print(f"There are total {dup_rows_in_df} duplicate rows in this dataset")

There are total 0 duplicate rows in this dataset

Q10. Please display the top 20 app categories ranked by their installation counts.?

To address this question, we’ll need to take some additional steps:

Convert the ‘Installs’ column into integers

Some apps have a value of -2147483648 in the ‘Installs’ column, which Google doesn’t want to display. We need to replace this value with the maximum value.

# Convert 'Installs' column to integer
df['Installs'] = df['Installs'].astype(int)

# Find the maximum value in the 'Installs' column
max_installs = df['Installs'].max()

# Replace '-2147483648' with the maximum value
df['Installs'].replace(-2147483648, max_installs, inplace=True)

top_20_categories_df = df.groupby('Category')['Installs'].nlargest(1)
print(top_20_categories_df)

Category
Action 57901 500000000
Adventure 25699 100000000
Arcade 785381 1000000000
Art & Design 578576 100000000
Auto & Vehicles 1623195 1000000000
Beauty 2204384 50000000
Board 1769868 500000000
Books & Reference 489887 1000000000
Business 1852547 1000000000
Card 769499 100000000
Casino 480340 50000000
Casual 285457 1000000000
Comics 1155395 50000000
Communication 352159 1000000000
Dating 1846598 100000000
Education 121984 100000000
Educational 335841 100000000
Entertainment 36844 1000000000
Events 395464 10000000
Finance 266378 100000000
Food & Drink 980170 100000000
Health & Fitness 187863 1000000000
House & Home 2225953 50000000
Libraries & Demo 2098884 50000000
Lifestyle 89747 500000000
Maps & Navigation 1205130 500000000
Medical 54453 10000000
Music 220344 100000000
Music & Audio 959700 1000000000
News & Magazines 415321 1000000000
Parenting 322287 10000000
Personalization 1429190 1000000000
Photography 52306 1000000000
Productivity 15825 1000000000
Puzzle 27854 100000000
Racing 1914941 500000000
Role Playing 498916 100000000
Shopping 2138397 500000000
Simulation 38146 100000000
Social 64838 1000000000
Sports 2062301 500000000
Strategy 422719 500000000
Tools 336834 1000000000
Travel & Local 495078 1000000000
Trivia 1398872 100000000
Video Players & Editors 167269 1000000000
Weather 1597264 500000000
Word 1827693 100000000
Name: Installs, dtype: int32

For easier comprehension, let’s plot the top ten categories.

top_10_categories_df = df.groupby('Category')['Installs'].sum().nlargest(10).reset_index()

# Create the bar plot
plt.figure(figsize=(14, 6))
sns.barplot(data=top_10_categories_df, x='Category', y='Installs', palette="plasma")
plt.ylabel('Total Installs')
plt.xlabel('Category')
plt.title('Top 10 Categories with Highest Total Installs')
plt.xticks(rotation=45, ha='right', fontsize=10)
plt.tight_layout()
plt.show()

According to this plot, the Tools category has the highest number of installs.

Q11. Please present the top 10 app categories with the highest average ratings?

# Category with highest average Rating
top_10_categories_highest_avg_rating = df.groupby('Category')['Rating'].mean().sort_values(ascending=False).head(10)
# Print the result
print(top_10_categories_highest_avg_rating)

Category
Role Playing 3.372444
Casino 3.279506
Simulation 3.206113
Weather 3.121011
Card 3.087922
Racing 2.961094
Video Players & Editors 2.904680
Word 2.902717
Strategy 2.886392
Comics 2.869888
Name: Rating, dtype: float64

Q12. Please display the average price of apps across different categories?

# Group by category and calculate the mean price
average_price_by_category = df.groupby('Category')['Price'].mean().reset_index()

# Print the average price of apps in each category
print(average_price_by_category)

               Category     Price

0 Action 0.066630
1 Adventure 0.180010
2 Arcade 0.096587
3 Art & Design 0.088625
4 Auto & Vehicles 0.116687
5 Beauty 0.005154
6 Board 0.170242
7 Books & Reference 0.193293
8 Business 0.056066
9 Card 0.130445
10 Casino 0.071434
11 Casual 0.048496
12 Comics 0.053878
13 Communication 0.042648
14 Dating 0.084517
15 Education 0.163200
16 Educational 0.171233
17 Entertainment 0.050822
18 Events 0.003468
19 Finance 0.052118
20 Food & Drink 0.020124
21 Health & Fitness 0.098702
22 House & Home 0.019028
23 Libraries & Demo 0.041754
24 Lifestyle 0.075994
25 Maps & Navigation 0.145898
26 Medical 0.713524
27 Music 0.063404
28 Music & Audio 0.042218
29 News & Magazines 0.006752
30 Parenting 0.070506
31 Personalization 0.107757
32 Photography 0.063086
33 Productivity 0.133512
34 Puzzle 0.086171
35 Racing 0.033536
36 Role Playing 0.311013
37 Shopping 0.008316
38 Simulation 0.109991
39 Social 0.040037
40 Sports 0.226809
41 Strategy 0.223534
42 Tools 0.147121
43 Travel & Local 0.073947
44 Trivia 0.031840
45 Video Players & Editors 0.122761
46 Weather 0.146946
47 Word 0.099919

To enhance understanding, let’s graph the average prices of apps in each category.

# Calculate the average price of apps in each category using groupby
average_price_by_category = df.groupby('Category')['Price'].mean()

# Plotting the average prices across categories
plt.figure(figsize=(12, 6))
average_price_by_category.sort_values(ascending=False).plot(kind='bar', color='blue')
plt.xlabel('App Category')
plt.ylabel('Average Price')
plt.title('Average App Prices Across Categories')
plt.show()

According to this plot highest average prices of apps are in Medical category.

Q13. Please show me the top 10 app developers along with the number of apps they have developed?

# Count the number of apps produced by each developer
top_10_developers = df['Developer Id'].value_counts().head(10)

# Print the result
print("Top 10 app-producing developers:")
print(top_10_developers)

Top 10 app-producing developers:
Subsplash Inc 5422
TRAINERIZE 5153
ChowNow 4865
OrderYOYO 2884
Phorest 2821
BH App Development Ltd 2453
Sharefaith 2077
Flipdish 1969
J&M Studio 1942
CyJ Studio 1741
Name: Developer Id, dtype: int64

Q14. Which Android version is most prevalent among top-rated apps?

# Most common Android version among top-rated apps
top_rated_apps = df[df['Rating'] >= 4.5]
most_common_android_version = top_rated_apps.groupby('Minimum Android')['App Name'].count().idxmax()
print(f"The most common Android version among top-rated apps is: {most_common_android_version}")

The most common Android version among top-rated apps is: 4.1 and up

Q15. Please display the top 10 most common Android versions among top-rated apps?

# Get the top 10 most common Android versions among top-rated apps
top_10_android_versions = top_rated_apps['Minimum Android'].value_counts().head(10)

# Plotting with seaborn
plt.figure(figsize=(12, 6))  # Adjust the figure size as needed
sns.barplot(x=top_10_android_versions.index, y=top_10_android_versions.values, color='blue')
plt.title('Top 10 Most Common Android Versions Among Top-rated Apps')
plt.xlabel('Android Version')
plt.ylabel('Number of Apps')
plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability
plt.grid(axis='y')  # Add gridlines only along the y-axis
plt.tight_layout()
plt.show()

Q16. Please show me the top 10 most installed apps across all categories?

top_10_installed_apps = df.sort_values(by='Installs', ascending=False).head(10)[['App Name', 'Category']]
print(top_10_installed_apps)

App Name Category

605097 Messages Communication
1623195 Android Auto Auto & Vehicles
64838 TikTok Social
785381 Subway Surfers Arcade
1425404 Android System WebView Tools
1870890 YouTube Music Music & Audio
2049898 Google Duo – High Quality Video Calls Communication
752600 Google Play Services for AR Tools
2148656 Google Play services Tools
187863 Samsung Health Health & Fitness

Q17. Please show me the top 5 highest-rated paid apps along with their ratings and prices?

# Assuming df is your DataFrame containing the Google Play Store data
top_5_paid_apps = df[df['Price'] > 0].nlargest(5, 'Rating')[['App Name', 'Rating', 'Price']]

print("Top 5 Paid Apps with Highest Ratings:")
print(top_5_paid_apps)

Top 5 Paid Apps with Highest Ratings:
App Name Rating Price
7718 Iqbaliyat (Urdu) 5.0 0.99
10933 Neo Widgets for KWGT 5.0 1.49
15074 Forest Kitten Live Wallpaper 5.0 1.99
15540 DES 5.0 5.49
24379 Приемка квартиры 5.0 2.49

Q18. Please show me the top 5 highest-rated free apps along with their ratings?

# Assuming df is your DataFrame containing the Google Play Store data
top_5_free_apps = df[df['Price'] == 0].nlargest(5, 'Rating')[['App Name', 'Rating']]

print("Top 5 Free Apps with Highest Ratings:")
print(top_5_free_apps)

Top 5 Free Apps with Highest Ratings:
App Name Rating
3 Smart City Trichy Public Service Vehicles 17UC… 5.0
17 All in one shopping app 5.0
42 Niagara Falls Wallpapers 5.0
43 Extrude Balance 5.0
72 Triple Point Academy 5.0

Q19. Please provide the count of apps in each category

df.groupby('Category').size().sort_values(ascending= False)

Category
Education 240530
Music & Audio 154689
Tools 143363
Business 143227
Entertainment 137966
Lifestyle 118145
Books & Reference 116581
Personalization 88981
Health & Fitness 83240
Productivity 79287
Shopping 75132
Food & Drink 73766
Travel & Local 67106
Finance 65211
Arcade 53514
Puzzle 50972
Casual 50596
Communication 47979
Sports 47334
Social 44635
News & Magazines 42707
Photography 35499
Medical 31911
Action 27403
Maps & Navigation 26640
Simulation 23198
Adventure 23116
Educational 21205
Art & Design 18465
Auto & Vehicles 18193
House & Home 14314
Video Players & Editors 13995
Events 12795
Trivia 11760
Beauty 11712
Board 10561
Racing 10330
Role Playing 9958
Word 8614
Strategy 8480
Card 8155
Weather 7220
Dating 6493
Libraries & Demo 5176
Casino 5060
Music 4186
Parenting 3793
Comics 2856
dtype: int64

Q20. Determine the category with the highest prices among paid apps.

# Filter for paid apps
paid_apps_df = df[df['Free'] == False]

# Group by category and calculate the average price for each category
average_prices = paid_apps_df.groupby('Category')['Price'].mean()

# Identify the category with the highest average price
most_expensive_category = average_prices.idxmax()
max_average_price = average_prices.max()

# Print the result
print(f"The most expensive category among paid apps is '{most_expensive_category}' with an average price of ${max_average_price:.2f}")

The most expensive category among paid apps is ‘Dating’ with an average price of $23.86

Q21. Please display the highest-priced apps within each category.

top_priced_apps = df.loc[df.groupby('Category')['Price'].idxmax()]
top_priced_apps[['Category', 'App Name', 'Price']].sort_values(by='Price', ascending=False)

	Category	App Name	Price
542844	Productivity	MESH Connect	400.00
1534601	Lifestyle	Why Not	399.99
919230	Entertainment	Luxsure	399.99
642302	Arcade	Challenge Impossible Ball for you	399.99
20069	Tools	TEST EGY	399.99
1276472	Social	RichWall	399.99
826958	Shopping	Premium Luxury Watches – Luxury Watches Brands	399.99
1395677	Board	Most expensive word game	399.99
650380	Books & Reference	secret of life	399.99
267948	Business	Taxes	399.99
1440576	Puzzle	Plasma duct – Premium Game	399.99
814716	Casual	Most Expensive Clicker	399.99
1854263	Music & Audio	AudD	399.99
1633789	Maps & Navigation	Точки интереса	399.99
1908008	Dating	Nu Media TV Live	399.99
787211	Health & Fitness	Acid Reflux Treatment	399.99
1600903	Word	Most Expensive Wordsearch	384.99
535840	Simulation	Presidential Election Campaign	379.99
1250613	Medical	Autism & Pervasive Developmental Disorders 4e	365.99
1691339	Education	PSC Login	364.99
1632424	Art & Design	10.000 Moving Cities – Same but Different	354.99
675663	Sports	Mega Tips Bet Premium (LifeTime)	338.99
1138115	Educational	Math vs Bath	294.99
1227026	Auto & Vehicles	REPUVE y mas… PRO	294.99
765248	Personalization	INFINITY STONES	279.99
987632	Food & Drink	how to dressed up Low carb Vietnamese pho	249.99
2295380	Finance	VahiKhatu – Track Borrowed / Lent Money	199.99
1475691	Photography	LockMyPix Photo Vault PRO: Hide Photos and Videos	162.99
733946	Weather	Weather – Routing – Navigation	109.99
1952542	Communication	Autocopy	99.99
569728	Video Players & Editors	Training Avid Media Composer 7	99.99
1339959	Card	The Preflop Advantage	74.99
701902	Action	INFESTED OLD – Horror Game	59.99
1239356	Role Playing	MR Beast ( Fan Game )	49.99
90195	Travel & Local	PathAway PRO Outdoor Navigator	44.99
623250	Casino	Blackjack Verite Games	34.99
750936	Adventure	英雄出征2	29.99
1895062	Trivia	The Jackbox Party Pack 5	29.99
1216057	Strategy	WARSURGE LIFETIME	21.99
221603	Libraries & Demo	Hide Something – License	19.99
1615614	House & Home	Cannabis Grow App Garden Plants Guide Tool Pro	17.99
1203626	Beauty	Golden Ratio Face – Beauty Analysis & Beauty Tips	14.99
697570	Events	Awe	11.99
2183125	Parenting	Bit Guardian Parental Control – For Parents	10.99
24606	Racing	Virtual Race Car Engineer 2018	9.99
1493937	News & Magazines	Hn3 news	9.99
1763904	Comics	Perfect Viewer Donation 3	9.99
1799838	Music	Café Twilight	9.49

Q22. Please show me the top 10 most rated apps in this dataset with the maximum number of user ratings.

top_rated_apps = df.sort_values(by='Rating Count', ascending=False).head(10)[['App Name', 'Rating Count', 'Category']]

print("Top 10 Rated Apps:")
top_rated_apps

	App Name	Rating Count	Category
384293	WhatsApp Messenger	138557570.0	Communication
303875	Instagram	120206190.0	Social
2216065	Facebook	117850066.0	Social
878819	YouTube	112440547.0	Video Players & Editors
243569	Garena Free Fire – Rampage	89177097.0	Action
2089577	Messenger – Text and Video Chat for Free	78563229.0	Communication
422719	Clash of Clans	56025424.0	Strategy
57901	PUBG MOBILE – Traverse	37479011.0	Action
64838	TikTok	36446381.0	Social
1825521	Google Photos	35369236.0	Photography

Q23. Let’s find out the number of users for each rating to understand how many apps received ratings

rating_counts = df['Rating'].value_counts().reset_index()
rating_counts

	index	Rating
0	0.0	1055706
1	5.0	99863
2	4.2	87820
3	4.4	86151
4	4.3	83130
5	4.6	78177
6	4.5	76632
7	4.1	69583
8	4.0	67219
9	4.7	62127
10	4.8	61010
11	3.9	55575
12	3.8	53760
13	4.9	44430
14	3.7	43208
15	3.6	35920
16	2.9	34060
17	3.5	31560
18	3.4	28849
19	3.3	22560
20	3.2	21426
21	3.0	17230
22	3.1	15501
23	2.8	12199
24	2.6	9180
25	2.7	9175
26	2.5	6922
27	2.4	6031
28	2.3	5392
29	2.2	4932
30	2.0	3791
31	2.1	3659
32	1.8	2941
33	1.9	2574
34	1.7	1919
35	1.6	1635
36	1.5	1154
37	1.4	1005
38	1.0	709
39	1.3	571
40	1.2	528
41	1.1	235

Q24. Please display the year-on-year comparison of apps per content rating?

# Assuming df is your DataFrame containing the Google Play Store data

# Extracting the year from the 'Last Updated' column
df['Year'] = pd.to_datetime(df['Last Updated']).dt.year

# Grouping the data by content rating and year, and counting the number of apps in each group
apps_per_content_rating_year = df.groupby(['Content Rating', 'Year']).size().unstack(fill_value=0)

# Displaying the result
print("Year-on-Year Comparison of Apps per Content Rating:")
print(apps_per_content_rating_year)

Year-on-Year Comparison of Apps per Content Rating:
Year 2009 2010 2011 2012 2013 2014 2015 2016 2017 \
Content Rating
Adults only 18+ 0 0 0 0 0 1 2 3 8
Everyone 13 215 854 2238 6616 15799 35231 60510 122987
Everyone 10+ 0 2 9 23 114 319 675 1117 2101
Mature 17+ 0 0 15 19 59 160 406 930 1794
Teen 0 4 19 45 200 614 1944 3742 7958
Unrated 0 1 1 2 19 59 40 5 13

Year 2018 2019 2020 2021
Content Rating
Adults only 18+ 5 21 41 53
Everyone 199650 378002 626442 567374
Everyone 10+ 2676 6457 10001 10267
Mature 17+ 4788 10353 19993 21610
Teen 15387 36175 67625 62229
Unrated 5 9 0 0

Q25. Please show me Top 10 Years with the Highest Number of App Releases.

# Assuming you have a DataFrame named df with the 'Released' column containing the release dates of the apps

# Convert 'Released' column to datetime format
df['Released'] = pd.to_datetime(df['Released'])

# Extract the release year and create a new column 'Year_Release'
df['Year_Release'] = df['Released'].dt.strftime('%Y')

# Group by 'Year_Release' and count the number of apps released in each year
cnt_year_app_Release = df.groupby('Year_Release').size().reset_index(name='Count')

# Sort the result by count in descending order and display the top 10 years
top_10_years = cnt_year_app_Release.head(20).sort_values(by='Count', ascending=False)

print(top_10_years)

Year_Release Count
10 2020 612959
9 2019 479031
8 2018 334028
7 2017 259377
11 2021 179794
6 2016 166811
5 2015 115287
4 2014 71718
3 2013 42405
2 2012 25665
1 2011 14342
0 2010 4632

We can create a bar plot illustrating the Top 10 Years with the Highest Number of App Releases.

plt.figure(figsize=(12, 6))
plt.bar(top_10_years['Year_Release'], top_10_years['Count'], color='blue')
plt.title('Top 10 Years with Highest Number of App Releases')
plt.xlabel('Year')
plt.ylabel('Number of App Releases')
plt.xticks(rotation=45)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

Q26. Please present a scatter plot to illustrate any relationship between user ratings and app prices?

# Plotting the relationship between user ratings and app prices
plt.figure(figsize=(10, 6))
sns.scatterplot(data=df, x='Price', y='Rating', color='blue')
plt.xlabel('Price')
plt.ylabel('Rating')
plt.title('Relationship Between User Ratings and App Prices')
plt.grid(False)
plt.tight_layout()
# Save the plot as an image file
plt.savefig('user_ratings_vs_app_prices.png')
plt.show()

Q27. Please create a scatter plot to visualize if there is any relationship between rating count and app size?

# Plotting the scatter plot
plt.figure(figsize=(12, 6))
sns.scatterplot(x='Size_in_Mb', y='Rating Count', data=df, alpha=0.7, color='blue')
plt.title('Correlation Between App Size and Rating Count', fontsize=16)
plt.xlabel('Size (MB)', fontsize=14)
plt.ylabel('Rating Count', fontsize=14)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.grid(True)


# Show the plot
plt.show()

Q28. Show me top 5 Puzzle apps with highest ratings.

# Assuming df is your DataFrame containing the Google Play Store data
top_5_puzzle_apps = df[df['Category']== 'Puzzle'].nlargest(5, 'Rating')[['App Name', 'Rating']]

print("Top 5 Puzzle Apps with Highest Ratings:")
print(top_5_puzzle_apps)

Top 5 Puzzle Apps with Highest Ratings:
App Name Rating
1027 Coptic Memory Game 5.0
1257 Juegos Turismo Villena 5.0
3385 Mars Bubbles 5.0
4385 Neuu Scsim 5.0
4663 2048 Challenge 5.0

Q29. Show me top 5 Medical apps with highest ratings.

# Assuming df is your DataFrame containing the Google Play Store data
top_5_medical_apps = df[df['Category']== 'Medical'].nlargest(5, 'Rating')[['App Name', 'Rating']]

print("Top 5 Medical Apps with Highest Ratings:")
print(top_5_medical_apps)

Top 5 Medical Apps with Highest Ratings:
App Name Rating
1263 CALIPER App 5.0
4904 Argon 5.0
5398 Auxein Medical 5.0
6472 El Menesy Pharmacies 5.0
9030 Ecg.tips 5.0

Q30. Please generate a bar plot showing the year-on-year breakdown of the top 5 categories based on app prices?

Rev_per_Genre = df.groupby(['Year_Release','Category' ])[['Price']].sum()


top5_genres = (Rev_per_Genre
                       .sort_values(['Year_Release', 'Price'], ascending=[True, False])
                       .groupby('Year_Release')
                       .head(5))

# Define a colormap: To present each category with a different color 
cmap = plt.get_cmap('tab20')


# Group by year and primary genre, and plot a stacked bar chart
top5_genres.groupby(['Year_Release', 'Category'])['Price'].sum().unstack().plot(kind='bar', stacked=True, title= "Year on Year break down of top-5 Category on App Price", cmap=cmap)

# Customize the plot
plt.xlabel('Year')
plt.ylabel('Total Price')
plt.legend(title='Category', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()

While this wasn’t a thorough analysis of the Google Play Store, we still gathered a lot of information. The depth of our exploration often depends on why we’re doing it in the first place. Our motive guides how deep we dive into the data. If we’re researching a particular category of apps, then we can explore more deeply within that specific category.

EDA Google Play App Store