"Data Analysis: Developer 101 | A Step Forward with Flutter"¶

"A Crisp analysis over available data from students registrations and attendees information from GoToMeeting"

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author: Teja Kummarikuntla

Developer 101 | A Step Forward with Flutter¶

The following notebook is an analysis of an online webinar organised by Sathyabama Coding Club

The data used here is private

In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")

%matplotlib inline

In [2]:

reg_data = pd.read_excel("Developer 101 _ A step Forward with Flutter (Responses) (1).xlsx")
ui_data = pd.read_excel("Developer 101 _ A Step Forward with Flutter Attendees_Cleanded_data.xls")

In [87]:

SESSION_DURATION = ui_data['Time in Session (minutes)'].max()
print("Session Duration in Minutes: ", SESSION_DURATION)

Session Duration in Minutes:  125

Analysis on Registration Data¶

Univariate Analysis¶

In [84]:

no_of_regs = len(reg_data)
REG_COUNT = no_of_regs
print("No of Registrations : ", REG_COUNT)

No of Registrations :  73

Observation:¶

No of registrations: 73

In [10]:

reg_data.Batch.value_counts()

Out[10]:

2022            27
2021            22
2023            13
Professional    11
Name: Batch, dtype: int64

In [12]:

sns.countplot(x="Batch", data=reg_data)
plt.title("Barplot on Academic year participation")

Out[12]:

Text(0.5, 1.0, 'Barplot on Academic year participation')

Observation¶

Students from 2022 are higher than 2021 and 2023 - 2022 >> 2021 >> 2023 > Professional

In [29]:

sns.countplot(x="Have you ever built a web/mobile application?", data=reg_data)
plt.title("Barplot over the background of participants in web/mobile app devlopment")

Out[29]:

Text(0.5, 1.0, 'Barplot over the background of participants in web/mobile app devlopment')

In [24]:

web_mob_yes = reg_data["Have you ever built a web/mobile application?"].value_counts()[1]
web_mob_no = reg_data["Have you ever built a web/mobile application?"].value_counts()[0]
print("The percentage of people joined the webinar who has prior exprience on Web/Android Dev :", 
      (web_mob_yes/no_of_regs)*100)
print("The percentage of people joined the webinar who has no prior exprience on Web/Android Dev :", 
      (web_mob_no/no_of_regs)*100)

The percentage of people joined the webinar who has prior exprience on Web/Android Dev : 31.506849315068493
The percentage of people joined the webinar who has no prior exprience on Web/Android Dev : 68.4931506849315

In [31]:

sns.countplot(x="Knowledge of Dart programming language ", 
              data=reg_data)

Out[31]:

<matplotlib.axes._subplots.AxesSubplot at 0x1755d3a0088>

In [42]:

reg_data["Where do you wish to use Flutter?"].value_counts().plot(kind='barh')

Out[42]:

<matplotlib.axes._subplots.AxesSubplot at 0x1755da27308>

Observation:

Majority of the participants Wished for Learning Flutter for Mobile App Development

Analysis on Attendes data¶

In [43]:

ui_data.head()

Out[43]:

	Name	Email Address	Join Time	Leave Time	Time in Session (minutes)
0	AJ	NaN	7:33 AM	8:16 AM	43
1	AJ	NaN	7:30 AM	7:32 AM	1
2	AJ	NaN	8:28 AM	9:13 AM	44
3	ANKITKUMAR SINGH	ankitk.as51@gmail.com	7:27 AM	7:43 AM	15
4	AR	aniket.rahalkar99@gmail.com	7:36 AM	7:39 AM	3

In [44]:

ATTENDEES_COUNT = len(ui_data['Name'].value_counts())
ATTENDEES_COUNT

Out[44]:

Observation¶

Number of attendees without duplicates: 45

In [47]:

len(ui_data.groupby(by=ui_data.Name, axis=1).sum())

Out[47]:

In [48]:

ui_data.groupby(['Name', 'Time in Session (minutes)']).sum().iloc[:,:0]

Out[48]:


Name	Time in Session (minutes)
AJ	1
	43
	44
ANKITKUMAR SINGH	15
AR	3
Abdultaiyeb Chechatwala	0
Abhiram	40
Abhiram	52
Abhishek Kumar	104
Aditya VijayKumar Singh	31
Aishwarya Pangarikar	81
Akash M	100
Antariksh Pratham	98
Athul	0
Athul	42
Ayush Shekhar	24
Ayush Shekhar	85
Ayush Sonare Ayush Sonare	38
Deepshikha	37
Dewashish Kumar	81
Dewashish kumar	23
HAMID HUSSAIN	66
JISS JOHNSON	0
Jiss Johnson	5
Jiss Johnson	98
Madallapalli sushanth	23
Maithili	3
Maithili	95
Mohit Kumar Patnaik	105
Mugunthan	1
Neha Rathi Neha Rathi	1
PALAKULA SAI TARUN	92
Psyco	19
Rohit	4
Rohit	105
Sanjana Birari	3
Sasank	59
Sonal Agrawal	16
Sonal Agrawal	51
Sriram Krishna	123
Suchismita Chakraborti	120
Surya Deepta Mazumdar	24
Surya Deepta Mazumdar	38
Swarnima Shishodia	12
Swarnima Shishodia	78
Teja Kummarikuntla	125
Vaibhav Mishra	64
Vaibhavkondabathini	0
You Know Who I Am	111
Zubair	9
Zubair	11
dhruv	72
kuro	59
neeraj jayaram	3
neeraj jayaram	104
reconnecting....	111
soundharya	86
tanvi tanvi	42

In [49]:

(ui_data['Time in Session (minutes)'] == ui_data['Time in Session (minutes)'].iloc[0]).all()

Out[49]:

False

In [50]:

def time_agg(group_series):
    if (group_series==group_series.iloc[0]).all():
        return group_series.iloc[0]
    else:
        return group_series.sum()

In [51]:

ui_data.groupby('Name', as_index=False).agg(time_agg)[['Name', 'Join Time', 'Leave Time', 'Time in Session (minutes)']]

Out[51]:

	Name	Join Time	Leave Time	Time in Session (minutes)
0	AJ	7:33 AM7:30 AM8:28 AM	8:16 AM7:32 AM9:13 AM	88
1	ANKITKUMAR SINGH	7:27 AM	7:43 AM	15
2	AR	7:36 AM	7:39 AM	3
3	Abdultaiyeb Chechatwala	7:27 AM	7:28 AM	0
4	Abhiram	7:30 AM8:20 AM	8:10 AM9:13 AM	92
5	Abhishek Kumar	7:28 AM	9:13 AM	104
6	Aditya VijayKumar Singh	7:27 AM	7:59 AM	31
7	Aishwarya Pangarikar	7:36 AM	8:57 AM	81
8	Akash M	7:26 AM	9:07 AM	100
9	Antariksh Pratham	7:07 AM	8:46 AM	98
10	Athul	8:30 AM7:47 AM	8:31 AM8:30 AM	42
11	Ayush Shekhar	7:23 AM7:47 AM	7:47 AM9:13 AM	109
12	Ayush Sonare Ayush Sonare	8:31 AM	9:10 AM	38
13	Deepshikha	7:56 AM	8:33 AM	37
14	Dewashish Kumar	7:52 AM	9:13 AM	81
15	Dewashish kumar	7:29 AM	7:52 AM	23
16	HAMID HUSSAIN	7:26 AM	8:32 AM	66
17	JISS JOHNSON	7:32 AM	7:33 AM	0
18	Jiss Johnson	7:30 AM7:34 AM	7:36 AM9:12 AM	103
19	Madallapalli sushanth	7:29 AM	7:53 AM	23
20	Maithili	7:33 AM7:37 AM	7:37 AM9:13 AM	98
21	Mohit Kumar Patnaik	7:27 AM	9:13 AM	105
22	Mugunthan	7:31 AM	7:32 AM	1
23	Neha Rathi Neha Rathi	7:34 AM	7:35 AM	1
24	PALAKULA SAI TARUN	7:36 AM	9:08 AM	92
25	Psyco	7:53 AM	8:13 AM	19
26	Rohit	7:19 AM9:05 AM	9:04 AM9:10 AM	109
27	Sanjana Birari	7:27 AM	7:31 AM	3
28	Sasank	7:57 AM	8:57 AM	59
29	Sonal Agrawal	7:32 AM8:21 AM	7:48 AM9:13 AM	67
30	Sriram Krishna	7:10 AM	9:13 AM	123
31	Suchismita Chakraborti	7:12 AM	9:13 AM	120
32	Surya Deepta Mazumdar	7:58 AM8:49 AM	8:37 AM9:13 AM	62
33	Swarnima Shishodia	7:14 AM7:27 AM	7:26 AM8:45 AM	90
34	Teja Kummarikuntla	7:07 AM	9:13 AM	125
35	Vaibhav Mishra	7:40 AM	8:44 AM	64
36	Vaibhavkondabathini	7:34 AM	7:34 AM	0
37	You Know Who I Am	7:21 AM	9:13 AM	111
38	Zubair	7:08 AM7:31 AM	7:17 AM7:43 AM	20
39	dhruv	7:39 AM	8:52 AM	72
40	kuro	7:35 AM	8:34 AM	59
41	neeraj jayaram	7:24 AM7:28 AM	7:28 AM9:13 AM	107
42	reconnecting....	7:21 AM	9:13 AM	111
43	soundharya	7:25 AM	8:52 AM	86
44	tanvi tanvi	7:29 AM	8:11 AM	42

In [74]:

atten_group_df = ui_data[['Name', 'Time in Session (minutes)', 'Email Address']].groupby('Name', as_index=False).agg(time_agg)
atten_group_df.sort_values(by=['Time in Session (minutes)'],ascending=False, inplace=True)

In [83]:

sns.factorplot(x="Name", y="Time in Session (minutes)", 
               data=atten_group_df, kind="bar", 
               size = 15, aspect=2,
               palette = "muted")

# for value in plot:
#     height = value.get_height()
#     plt.text(value.get_x() + value.get_width()/2.,
#              1.002*height,'%d' % int(height), ha='center', va='bottom')

plt.xticks(rotation=45);

Individual time spent analysis of attendes

In [76]:

sns.factorplot(x="Name", y="Time in Session (minutes)", 
               data=atten_group_df[atten_group_df["Time in Session (minutes)"] >= SESSION_DURATION//2], 
               kind="bar", 
               size = 8, aspect=2,
               palette = "muted")

plt.xticks(rotation=45);

In [77]:

atten_group_df[atten_group_df["Time in Session (minutes)"] >= SESSION_DURATION//2][['Name', 'Time in Session (minutes)']].set_index('Name')

Out[77]:

	Time in Session (minutes)
Name
Teja Kummarikuntla	125
Sriram Krishna	123
Suchismita Chakraborti	120
You Know Who I Am	111
reconnecting....	111
Rohit	109
Ayush Shekhar	109
neeraj jayaram	107
Mohit Kumar Patnaik	105
Abhishek Kumar	104
Jiss Johnson	103
Akash M	100
Maithili	98
Antariksh Pratham	98
PALAKULA SAI TARUN	92
Abhiram	92
Swarnima Shishodia	90
AJ	88
soundharya	86
Dewashish Kumar	81
Aishwarya Pangarikar	81
dhruv	72
Sonal Agrawal	67
HAMID HUSSAIN	66
Vaibhav Mishra	64
Surya Deepta Mazumdar	62

In [78]:

atten_group_df

Out[78]:

	Name	Time in Session (minutes)
34	Teja Kummarikuntla	125
30	Sriram Krishna	123
31	Suchismita Chakraborti	120
37	You Know Who I Am	111
42	reconnecting....	111
26	Rohit	109
11	Ayush Shekhar	109
41	neeraj jayaram	107
21	Mohit Kumar Patnaik	105
5	Abhishek Kumar	104
18	Jiss Johnson	103
8	Akash M	100
20	Maithili	98
9	Antariksh Pratham	98
24	PALAKULA SAI TARUN	92
4	Abhiram	92
33	Swarnima Shishodia	90
0	AJ	88
43	soundharya	86
14	Dewashish Kumar	81
7	Aishwarya Pangarikar	81
39	dhruv	72
29	Sonal Agrawal	67
16	HAMID HUSSAIN	66
35	Vaibhav Mishra	64
32	Surya Deepta Mazumdar	62
28	Sasank	59
40	kuro	59
44	tanvi tanvi	42
10	Athul	42
12	Ayush Sonare Ayush Sonare	38
13	Deepshikha	37
6	Aditya VijayKumar Singh	31
19	Madallapalli sushanth	23
15	Dewashish kumar	23
38	Zubair	20
25	Psyco	19
1	ANKITKUMAR SINGH	15
27	Sanjana Birari	3
2	AR	3
23	Neha Rathi Neha Rathi	1
22	Mugunthan	1
17	JISS JOHNSON	0
36	Vaibhavkondabathini	0
3	Abdultaiyeb Chechatwala	0

In [79]:

len(atten_group_df[atten_group_df["Time in Session (minutes)"] >= SESSION_DURATION//2].set_index('Name')['Time in Session (minutes)'])

Out[79]:

In [85]:

registerd_attendes_ratio = (ATTENDEES_COUNT/REG_COUNT) * 100
print("Percentage of Students registered and attended the session {}".format(registerd_attendes_ratio))

Percentage of Students registered and attended the session 61.64383561643836

Summary [Report]¶

Registration Data Analysis

Name of the Event: Developer 101 | A Step Forward with Flutter
No of registrations: 73
Registration Count with Batch filter
- 2022 : 27
- 2021 : 22
- 2023 : 13
- Professional : 11
No of registrations With out prior knowledge on web/app Development : 50 [68.4931506849315%]
No of registrations With prior knowledge on web/app Development : 23 [31.506849315068493%]
No of registrations who are Beginners in Dart Programming language : 61
No of registrations who are Beginners in Dart Programming language : 12
Registrations wish to use Flutter for
- Mobile app development | 38
- Web app development | 13
- Mobile app development, Web app development, Desktop app development | 12
- Mobile app development, Web app development | 9
- Mobile app development, Desktop app development | 1

Webinar Attendes Data Analysis

No of Attendees: 45
No of students spent more than half in the session: 27
*Percentage of Students registered and attended the session 61.64383561643836%*
Students spent more than 55 minutes in the session Teja Kummarikuntla Sriram Krishna Suchismita Chakraborti You Know Who I Am reconnecting.... Rohit Ayush Shekhar neeraj jayaram Mohit Kumar Patnaik Abhishek Kumar Jiss Johnson Akash M Maithili Antariksh Pratham PALAKULA SAI TARUN Abhiram Swarnima Shishodia AJ soundharya Dewashish Kumar Aishwarya Pangarikar dhruv Sonal Agrawal HAMID HUSSAIN Vaibhav Mishra Surya Deepta Mazumdar