import pandas as pd

filepath = "C:\\Users\\abdelilah\\Desktop\\Python Projects\\Covid-19\\"

pd.set_option('display.max_rows', 10)

covid_df = pd.read_csv(filepath + "covid_19_data.csv", delimiter = ",")
covid_df

	Date	State	Region	Confirmed	Deaths	Recovered
0	4/29/2020	NaN	Afghanistan	1939	60	252
1	4/29/2020	NaN	Albania	766	30	455
2	4/29/2020	NaN	Algeria	3848	444	1702
3	4/29/2020	NaN	Andorra	743	42	423
4	4/29/2020	NaN	Angola	27	2	7
...	...	...	...	...	...	...
316	4/29/2020	Wyoming	US	545	7	0
317	4/29/2020	Xinjiang	Mainland China	76	3	73
318	4/29/2020	Yukon	Canada	11	0	0
319	4/29/2020	Yunnan	Mainland China	185	2	181
320	4/29/2020	Zhejiang	Mainland China	1268	1	1263

321 rows × 6 columns

covid_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 321 entries, 0 to 320
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype 
---  ------     --------------  ----- 
 0   Date       321 non-null    object
 1   State      140 non-null    object
 2   Region     321 non-null    object
 3   Confirmed  321 non-null    int64 
 4   Deaths     321 non-null    int64 
 5   Recovered  321 non-null    int64 
dtypes: int64(3), object(3)
memory usage: 15.2+ KB

values = dict()
for col in covid_df.columns:
    values[col] = covid_df[col].value_counts()

values

{'Date': 4/29/2020    321
 Name: Date, dtype: int64,
 'State': Grand Princess                  2
 Diamond Princess cruise ship    2
 Recovered                       2
 North Carolina                  1
 North Dakota                    1
                                ..
 Zhejiang                        1
 Shaanxi                         1
 Puerto Rico                     1
 New Hampshire                   1
 Arizona                         1
 Name: State, Length: 137, dtype: int64,
 'Region': US                 58
 Mainland China     31
 Canada             15
 France             11
 UK                 11
                    ..
 North Macedonia     1
 El Salvador         1
 Pakistan            1
 Slovakia            1
 Lithuania           1
 Name: Region, Length: 187, dtype: int64,
 'Confirmed': 11        5
 6         4
 16        4
 18        4
 8         3
          ..
 2727      1
 168       1
 299691    1
 1361      1
 510       1
 Name: Confirmed, Length: 282, dtype: int64,
 'Deaths': 0        53
 1        25
 3        15
 2        14
 6        13
          ..
 98        1
 92        1
 87        1
 3670      1
 26097     1
 Name: Deaths, Length: 142, dtype: int64,
 'Recovered': 0        78
 5         5
 8         5
 19        4
 6         4
          ..
 124       1
 63616     1
 131       1
 645       1
 1023      1
 Name: Recovered, Length: 195, dtype: int64}

---

First Task: Show the number of Confirmed, Deaths and Recovered cases in each Region

# i don't get what they mean exactly, it confused me, cuz if they want the info about each Region then it's already displayed
# in the dataframe. So i think i'll just assign the columns wanted in a new dataframe
# i think i get it now, the regions here mentioned many times, maybe this task wants me to gather all those regions into one 

cases_df = covid_df.drop(["Date", "State"], axis = 1)

cases_df["Region"].value_counts()

US                 58
Mainland China     31
Canada             15
France             11
UK                 11
                   ..
North Macedonia     1
El Salvador         1
Pakistan            1
Slovakia            1
Lithuania           1
Name: Region, Length: 187, dtype: int64

cases_df = cases_df.groupby('Region').sum()

cases_df

	Confirmed	Deaths	Recovered
Region
Afghanistan	1939	60	252
Albania	766	30	455
Algeria	3848	444	1702
Andorra	743	42	423
Angola	27	2	7
...	...	...	...
West Bank and Gaza	344	2	71
Western Sahara	6	0	5
Yemen	6	0	1
Zambia	97	3	54
Zimbabwe	32	4	5

187 rows × 3 columns

---

Second Task: Remove all the records where the Confirmed Cases is Less Than 10

less_10 = covid_df[covid_df["Confirmed"] >= 10]

less_10

	Date	State	Region	Confirmed	Deaths	Recovered
0	4/29/2020	NaN	Afghanistan	1939	60	252
1	4/29/2020	NaN	Albania	766	30	455
2	4/29/2020	NaN	Algeria	3848	444	1702
3	4/29/2020	NaN	Andorra	743	42	423
4	4/29/2020	NaN	Angola	27	2	7
...	...	...	...	...	...	...
316	4/29/2020	Wyoming	US	545	7	0
317	4/29/2020	Xinjiang	Mainland China	76	3	73
318	4/29/2020	Yukon	Canada	11	0	0
319	4/29/2020	Yunnan	Mainland China	185	2	181
320	4/29/2020	Zhejiang	Mainland China	1268	1	1263

304 rows × 6 columns

---

Third Task: In which Region, maximum number of Confirmed cases were recorded ?

cases_df["Confirmed"].idxmax()

'US'

cases_df.loc['US']

Confirmed    1039909
Deaths         60967
Recovered     120720
Name: US, dtype: int64

---

Fourth Task: In which Region, minimum number of Deaths cases were recorded ?

cases_df["Deaths"].idxmin()

'Bhutan'

cases_df.loc['Bhutan']

Confirmed    7
Deaths       0
Recovered    5
Name: Bhutan, dtype: int64

---

Fifth Task: How many Confirmed, Deaths & Recovered cases were reported from India till 29 April 2020 ?

cases_df.loc["India"]

Confirmed    33062
Deaths        1079
Recovered     8437
Name: India, dtype: int64

---

Sixth Task:
A- Sort the entire data wrt No. of Confirmed cases in ascending order.

cases_df.sort_values(by = ["Confirmed"], ascending = True)

	Confirmed	Deaths	Recovered
Region
Yemen	6	0	1
Western Sahara	6	0	5
Bhutan	7	0	5
Papua New Guinea	8	0	0
Sao Tome and Principe	8	0	4
...	...	...	...
UK	166441	26166	857
France	166543	24121	49118
Italy	203591	27682	71252
Spain	236899	24275	132929
US	1039909	60967	120720

187 rows × 3 columns

B- Sort the entire data wrt No. of Recovered cases in descending order.

cases_df.sort_values(by = ["Recovered"], ascending = False)

	Confirmed	Deaths	Recovered
Region
Spain	236899	24275	132929
US	1039909	60967	120720
Germany	161539	6467	120400
Mainland China	82862	4633	77610
Iran	93657	5957	73791
...	...	...	...
Cabo Verde	114	1	2
Yemen	6	0	1
South Sudan	34	0	0
Papua New Guinea	8	0	0
MS Zaandam	9	2	0

187 rows × 3 columns

---

#this is a useful way to get an idea about how dispersed the missing values are in the dataset

import matplotlib.pyplot as plt
import seaborn as sns

sns.heatmap(covid_df.isnull())

<AxesSubplot:>