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Task: Covid-19 Data Analysis¶

This notebook is used to understand the comprehension of Data Analysis techniques using Pandas library.¶

Data Source:¶

https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports

File naming convention¶

MM-DD-YYYY.csv in UTC.

Field description¶

Province_State: China - province name; US/Canada/Australia/ - city name, state/province name; Others - name of the event (e.g., "Diamond Princess" cruise ship); other countries - blank.
Country_Region: country/region name conforming to WHO (will be updated).
Last_Update: MM/DD/YYYY HH:mm (24 hour format, in UTC).
Confirmed: the number of confirmed cases. For Hubei Province: from Feb 13 (GMT +8), we report both clinically diagnosed and lab-confirmed cases. For lab-confirmed cases only (Before Feb 17), please refer to who_covid_19_situation_reports. For Italy, diagnosis standard might be changed since Feb 27 to "slow the growth of new case numbers." (Source)
Deaths: the number of deaths.
Recovered: the number of recovered cases.

Question 1¶

Read the dataset¶

In [1]:

Out[1]:

	FIPS	Admin2	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Recovered	Active	Combined_Key	Incident_Rate	Case_Fatality_Ratio
0	NaN	NaN	NaN	Afghanistan	2021-01-11 05:21:50	33.939110	67.709953	53489	2277	43948	7264.0	Afghanistan	137.403749	4.256950
1	NaN	NaN	NaN	Albania	2021-01-11 05:21:50	41.153300	20.168300	63595	1241	37648	24706.0	Albania	2209.847800	1.951411
2	NaN	NaN	NaN	Algeria	2021-01-11 05:21:50	28.033900	1.659600	102144	2807	69212	30125.0	Algeria	232.934026	2.748081
3	NaN	NaN	NaN	Andorra	2021-01-11 05:21:50	42.506300	1.521800	8586	85	7724	777.0	Andorra	11112.405358	0.989984
4	NaN	NaN	NaN	Angola	2021-01-11 05:21:50	-11.202700	17.873900	18193	416	13872	3905.0	Angola	55.354627	2.286594
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3972	NaN	NaN	NaN	Vietnam	2021-01-11 05:21:50	14.058324	108.277199	1514	35	1361	118.0	Vietnam	1.555396	2.311757
3973	NaN	NaN	NaN	West Bank and Gaza	2021-01-11 05:21:50	31.952200	35.233200	147400	1604	131117	14679.0	West Bank and Gaza	2889.393847	1.088195
3974	NaN	NaN	NaN	Yemen	2021-01-11 05:21:50	15.552727	48.516388	2104	611	1407	86.0	Yemen	7.054256	29.039924
3975	NaN	NaN	NaN	Zambia	2021-01-11 05:21:50	-13.133897	27.849332	27728	469	20598	6661.0	Zambia	150.827167	1.691431
3976	NaN	NaN	NaN	Zimbabwe	2021-01-11 05:21:50	-19.015438	29.154857	21477	507	12582	8388.0	Zimbabwe	144.500474	2.360665

3977 rows × 14 columns

Display the top 5 rows in the data¶

In [2]:

Out[2]:

	FIPS	Admin2	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Recovered	Active	Combined_Key	Incident_Rate	Case_Fatality_Ratio
0	NaN	NaN	NaN	Afghanistan	2021-01-11 05:21:50	33.93911	67.709953	53489	2277	43948	7264.0	Afghanistan	137.403749	4.256950
1	NaN	NaN	NaN	Albania	2021-01-11 05:21:50	41.15330	20.168300	63595	1241	37648	24706.0	Albania	2209.847800	1.951411
2	NaN	NaN	NaN	Algeria	2021-01-11 05:21:50	28.03390	1.659600	102144	2807	69212	30125.0	Algeria	232.934026	2.748081
3	NaN	NaN	NaN	Andorra	2021-01-11 05:21:50	42.50630	1.521800	8586	85	7724	777.0	Andorra	11112.405358	0.989984
4	NaN	NaN	NaN	Angola	2021-01-11 05:21:50	-11.20270	17.873900	18193	416	13872	3905.0	Angola	55.354627	2.286594

Show the information of the dataset¶

In [3]:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3977 entries, 0 to 3976
Data columns (total 14 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   FIPS                 3263 non-null   float64
 1   Admin2               3268 non-null   object 
 2   Province_State       3807 non-null   object 
 3   Country_Region       3977 non-null   object 
 4   Last_Update          3977 non-null   object 
 5   Lat                  3891 non-null   float64
 6   Long_                3891 non-null   float64
 7   Confirmed            3977 non-null   int64  
 8   Deaths               3977 non-null   int64  
 9   Recovered            3977 non-null   int64  
 10  Active               3976 non-null   float64
 11  Combined_Key         3977 non-null   object 
 12  Incident_Rate        3891 non-null   float64
 13  Case_Fatality_Ratio  3935 non-null   float64
dtypes: float64(6), int64(3), object(5)
memory usage: 435.1+ KB

Show the sum of missing values of features in the dataset¶

In [4]:

Out[4]:

FIPS                   714
Admin2                 709
Province_State         170
Country_Region           0
Last_Update              0
Lat                     86
Long_                   86
Confirmed                0
Deaths                   0
Recovered                0
Active                   1
Combined_Key             0
Incident_Rate           86
Case_Fatality_Ratio     42
dtype: int64

Question 2¶

Show the number of Confirmed cases by Country¶

In [5]:

Out[5]:

	Country_Region	Confirmed
0	Afghanistan	53489
1	Albania	63595
2	Algeria	102144
3	Andorra	8586
4	Angola	18193
...	...	...
186	Vietnam	1514
187	West Bank and Gaza	147400
188	Yemen	2104
189	Zambia	27728
190	Zimbabwe	21477

191 rows × 2 columns

Show the number of Deaths by Country¶

In [6]:

Out[6]:

	Country_Region	Deaths
0	Afghanistan	2277
1	Albania	1241
2	Algeria	2807
3	Andorra	85
4	Angola	416
...	...	...
186	Vietnam	35
187	West Bank and Gaza	1604
188	Yemen	611
189	Zambia	469
190	Zimbabwe	507

191 rows × 2 columns

Show the number of Recovered cases by Country¶

In [7]:

Out[7]:

	Country_Region	Recovered
0	Afghanistan	43948
1	Albania	37648
2	Algeria	69212
3	Andorra	7724
4	Angola	13872
...	...	...
186	Vietnam	1361
187	West Bank and Gaza	131117
188	Yemen	1407
189	Zambia	20598
190	Zimbabwe	12582

191 rows × 2 columns

Show the number of Active Cases by Country¶

In [8]:

Out[8]:

	Country_Region	Active
0	Afghanistan	7264
1	Albania	24706
2	Algeria	30125
3	Andorra	777
4	Angola	3905
...	...	...
186	Vietnam	118
187	West Bank and Gaza	14679
188	Yemen	86
189	Zambia	6661
190	Zimbabwe	8388

191 rows × 2 columns

Show the latest number of Confirmed, Deaths, Recovered and Active cases Country-wise¶

In [9]:

/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.

Out[9]:

	Country_Region	Confirmed	Deaths	Recovered	Active
0	Afghanistan	53489	2277	43948	7264
1	Albania	63595	1241	37648	24706
2	Algeria	102144	2807	69212	30125
3	Andorra	8586	85	7724	777
4	Angola	18193	416	13872	3905
...	...	...	...	...	...
186	Vietnam	1514	35	1361	118
187	West Bank and Gaza	147400	1604	131117	14679
188	Yemen	2104	611	1407	86
189	Zambia	27728	469	20598	6661
190	Zimbabwe	21477	507	12582	8388

191 rows × 5 columns

Question 3¶

Show the countries with no recovered cases¶

In [10]:

Out[10]:

	Country_Region	Recovered
16	Belgium	0
151	Serbia	0
165	Sweden	0
177	US	0

Show the countries with no confirmed cases¶

In [11]:

Out[11]:

	Country_Region	Confirmed

Show the countries with no deaths¶

In [12]:

Out[12]:

	Country_Region	Deaths
30	Cambodia	0
50	Dominica	0
75	Holy See	0
95	Laos	0
111	Marshall Islands	0
143	Saint Kitts and Nevis	0
145	Saint Vincent and the Grenadines	0
146	Samoa	0
157	Solomon Islands	0
172	Timor-Leste	0
184	Vanuatu	0

In [13]:

/usr/local/lib/python3.7/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.

Out[13]:

	Country_Region	Confirmed	Recovered
30	Cambodia	391	374
50	Dominica	106	98
75	Holy See	27	15
95	Laos	41	40
111	Marshall Islands	4	4
143	Saint Kitts and Nevis	34	31
145	Saint Vincent and the Grenadines	249	100
146	Samoa	2	2
157	Solomon Islands	17	10
172	Timor-Leste	49	41
184	Vanuatu	1	1

Question 4¶

Show the Top 10 countries with Confirmed cases¶

In [14]:

Out[14]:

	Last_Update	Confirmed	Deaths	Recovered
Country_Region
France	2021-01-10 05:22:12	2762120	67229	178036
United Kingdom	2021-01-10 05:22:12	2613838	70541	2850
Turkey	2021-01-10 05:22:12	2317118	22631	2190047
India	2021-01-10 05:22:12	1965556	50027	1861400
Argentina	2021-01-10 05:22:12	1714409	44417	1504330
Brazil	2021-01-10 05:22:12	1540513	48298	1350513
Poland	2021-01-10 05:22:12	1376389	31011	1113738
Iran	2021-01-10 05:22:12	1280438	56100	1067466
South Africa	2021-01-10 05:22:12	1214176	32824	956712
US	2021-01-10 05:22:12	907077	12090	0

Show the Top 10 Countries with Active cases¶

In [15]:

Out[15]:

	Last_Update	Confirmed	Deaths	Recovered	Active
Country_Region
United Kingdom	2021-01-10 05:22:12	2613838	70541	2850	2543297
France	2021-01-10 05:22:12	2762120	67229	178036	2516855
US	2021-01-10 05:22:12	907077	12090	0	894987
Peru	2021-01-10 05:22:12	454697	16923	966447	437774
Spain	2021-01-10 05:22:12	421295	11971	40736	368588
Serbia	2021-01-10 05:22:12	356125	3513	0	352612
Mexico	2021-01-10 05:22:12	364770	20251	1143254	346710
Poland	2021-01-10 05:22:12	1376389	31011	1113738	231640
South Africa	2021-01-10 05:22:12	1214176	32824	956712	224640
Netherlands	2021-01-10 05:22:12	214929	2963	5436	211966

Question 5¶

Plot Country-wise Total deaths, confirmed, recovered and active casaes where total deaths have exceeded 50,000¶

In [16]:

import matplotlib.pyplot as plt

In [17]:

/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  This is separate from the ipykernel package so we can avoid doing imports until

Question 6¶

Plot Province/State wise Deaths in USA¶

In [18]:

import plotly.express as px

In [19]:

covid_data= pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-09-2021.csv')

In [20]:

covid_data.columns

Out[20]:

Index(['FIPS', 'Admin2', 'Province_State', 'Country_Region', 'Last_Update',
       'Lat', 'Long_', 'Confirmed', 'Deaths', 'Recovered', 'Active',
       'Combined_Key', 'Incident_Rate', 'Case_Fatality_Ratio'],
      dtype='object')

In [21]:

Question 7¶

Plot Province/State Wise Active Cases in USA¶

In [22]:

Question 8¶

Plot Province/State Wise Confirmed cases in USA¶

In [23]:

/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning:

Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.

Question 9¶

Plot Worldwide Confirmed Cases over time¶

In [24]:

import plotly.express as px
import plotly.io as pio

In [25]:

/usr/local/lib/python3.7/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.

In [ ]: