import pandas as pd

df1 = pd.read_csv('respondents.csv')
df2 = pd.read_csv('states.csv')

df1

	Person_Key	Age_Key	Gender_Key	State_Key	Children	Salary	Opinion_Key
0	1	2	2	9	2	63017	5
1	2	2	2	10	3	100302	1
2	3	2	2	2	0	144043	5
3	4	1	2	2	0	36025	4
4	5	2	1	9	0	97543	3
...	...	...	...	...	...	...	...
394	395	2	2	2	0	60715	3
395	396	2	1	10	2	91760	2
396	397	2	2	1	1	82558	1
397	398	2	1	7	1	84880	5
398	399	2	2	4	2	76933	5

399 rows × 7 columns

df2

	State_Key	State_Name
0	1	Arizona
1	2	California
2	3	Florida
3	4	Illinois
4	5	Michigan
5	6	Minnesota
6	7	New York
7	8	Ohio
8	9	Texas
9	10	Virginia

result = pd.merge(df1,df2, on=["State_Key", "State_Key"])

result.head()

	Person_Key	Age_Key	Gender_Key	State_Key	Children	Salary	Opinion_Key	State_Name
0	1	2	2	9	2	63017	5	Texas
1	5	2	1	9	0	97543	3	Texas
2	8	2	2	9	1	101894	4	Texas
3	14	3	2	9	2	67748	3	Texas
4	15	1	2	9	2	47172	3	Texas

result = pd.merge(df1, df2, how='left', on=["State_Key", "State_Key"])
#can specific type of join with how option 'right', 'left', 'outer', 'inner'

result.head()

	Person_Key	Age_Key	Gender_Key	State_Key	Children	Salary	Opinion_Key	State_Name
0	1	2	2	9	2	63017	5	Texas
1	2	2	2	10	3	100302	1	Virginia
2	3	2	2	2	0	144043	5	California
3	4	1	2	2	0	36025	4	California
4	5	2	1	9	0	97543	3	Texas

df3 = pd.read_csv('states2.csv')

df3

	State_Key	State_Name
0	11	Alabama
1	12	Connecticut
2	13	Georgia
3	14	Indiana
4	15	Mississippi
5	16	Montana
6	17	New Jersey
7	18	Oklahoma
8	19	Tennessee
9	20	West Virginia

df4 = df2.append(df3, ignore_index=True)
df4

	State_Key	State_Name
0	1	Arizona
1	2	California
2	3	Florida
3	4	Illinois
4	5	Michigan
5	6	Minnesota
6	7	New York
7	8	Ohio
8	9	Texas
9	10	Virginia
10	11	Alabama
11	12	Connecticut
12	13	Georgia
13	14	Indiana
14	15	Mississippi
15	16	Montana
16	17	New Jersey
17	18	Oklahoma
18	19	Tennessee
19	20	West Virginia

df5 = pd.read_csv('states3.csv')
df5

	State_Key	State_Capital
0	1	Phoenix
1	2	Sacremento
2	3	Tallahassee
3	4	Springfield
4	5	Lansing
5	6	Saint Paul
6	7	Albany
7	8	Columbus
8	9	Austin
9	10	Richmond

df6 = pd.concat([df2,df5])
df6

	State_Key	State_Name	State_Capital
0	1	Arizona	NaN
1	2	California	NaN
2	3	Florida	NaN
3	4	Illinois	NaN
4	5	Michigan	NaN
5	6	Minnesota	NaN
6	7	New York	NaN
7	8	Ohio	NaN
8	9	Texas	NaN
9	10	Virginia	NaN
0	1	NaN	Phoenix
1	2	NaN	Sacremento
2	3	NaN	Tallahassee
3	4	NaN	Springfield
4	5	NaN	Lansing
5	6	NaN	Saint Paul
6	7	NaN	Albany
7	8	NaN	Columbus
8	9	NaN	Austin
9	10	NaN	Richmond

df6 = pd.concat([df2,df5],axis=1)
df6

	State_Key	State_Name	State_Key	State_Capital
0	1	Arizona	1	Phoenix
1	2	California	2	Sacremento
2	3	Florida	3	Tallahassee
3	4	Illinois	4	Springfield
4	5	Michigan	5	Lansing
5	6	Minnesota	6	Saint Paul
6	7	New York	7	Albany
7	8	Ohio	8	Columbus
8	9	Texas	9	Austin
9	10	Virginia	10	Richmond

df6 = pd.merge(df2,df5, on=["State_Key", "State_Key"])

df6

	State_Key	State_Name	State_Capital
0	1	Arizona	Phoenix
1	2	California	Sacremento
2	3	Florida	Tallahassee
3	4	Illinois	Springfield
4	5	Michigan	Lansing
5	6	Minnesota	Saint Paul
6	7	New York	Albany
7	8	Ohio	Columbus
8	9	Texas	Austin
9	10	Virginia	Richmond

Color palettes for plotting:

1) https://jiffyclub.github.io/palettable/matplotlib/ 2) https://jiffyclub.github.io/palettable/colorbrewer/ 3) https://github.com/dsc/colorbrewer-python

Plotting libraries:

1) https://analyticsindiamag.com/top-5-python-libraries-for-data-visualization/ 2) https://mode.com/blog/python-data-visualization-libraries/ 3) https://mode.com/blog/python-interactive-plot-libraries/

import datetime

x = datetime.datetime.now()
print(x)

2021-03-22 18:44:56.942221

print(x.year)
print(x.strftime("%A"))

2021
Monday

print(x.strftime("%b"))

Mar

x = datetime.datetime(2020, 5, 17)

print(x)

2020-05-17 00:00:00

from datetime import date, time, datetime
date(year=2020, month=1, day=31)

datetime.date(2020, 1, 31)

today = date.today()
today

datetime.date(2021, 3, 22)

date(year=2021, month=2, day=29)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-25-5bf98016428a> in <module>
----> 1 date(year=2021, month=2, day=29)

ValueError: day is out of range for month

datetime(year=2020, month=1, day=31, hour=13, minute=14, second=31)

datetime.datetime(2020, 1, 31, 13, 14, 31)

x = datetime.now()

y=datetime.now()

y-x

datetime.timedelta(seconds=14, microseconds=420885)

date.fromisoformat("2020-01-31")

datetime.date(2020, 1, 31)

date_string = "01-31-2020 14:45:37"
format_string = "%m-%d-%Y %H:%M:%S"
datetime.strptime(date_string, format_string)

datetime.datetime(2020, 1, 31, 14, 45, 37)

import numpy as np
mu, sigma = 0, 1000 # mean and standard deviation
N=1000
s = np.random.normal(mu, sigma, N)

df7 = pd.DataFrame(s, columns = ['Error'])
result['Error']=round(df7['Error'],0)
result.head()

	Person_Key	Age_Key	Gender_Key	State_Key	Children	Salary	Opinion_Key	State_Name	Error
0	1	2	2	9	2	63017	5	Texas	175.0
1	2	2	2	10	3	100302	1	Virginia	-1709.0
2	3	2	2	2	0	144043	5	California	-1379.0
3	4	1	2	2	0	36025	4	California	595.0
4	5	2	1	9	0	97543	3	Texas	884.0

result['Salary_New']=result['Salary']+result['Error']
result.head()

	Person_Key	Age_Key	Gender_Key	State_Key	Children	Salary	Opinion_Key	State_Name	Error	Salary_New
0	1	2	2	9	2	63017	5	Texas	175.0	63192.0
1	2	2	2	10	3	100302	1	Virginia	-1709.0	98593.0
2	3	2	2	2	0	144043	5	California	-1379.0	142664.0
3	4	1	2	2	0	36025	4	California	595.0	36620.0
4	5	2	1	9	0	97543	3	Texas	884.0	98427.0