#CodingForLife

[learncodes]

Learn Codes

🚀 Unlock the World of Coding with Learn Codes! 🚀

Are you ready to embark on an exciting coding journey from the comfort of your own home? Look no further! Learn Codes is here to make coding fun, engaging, and accessible to all ages. 🌟

👩‍💻 Our Online 1:1 Coding Classes offer: ✅ Personalized learning tailored to your pace and skill level ✅ Expert instructors with years of industry experience ✅ Interactive coding projects to boost your skills ✅ Flexible scheduling to fit your busy life

Don't miss out on this amazing opportunity to enhance your coding skills and unlock endless possibilities! 💻💡

🌟 Why Choose Learn Codes? 🌟

OnlineCodingClasses

LearnCoding

CodingForAll

CodingJourney

CodingMadeEasy

CodingFun

CodingSkills

CodeWithUs

Programming101

TechEnthusiasts

CodeWithConfidence

OnlineLearning

CodingCommunity

TechEducation

CodeToSuccess

CodingMentor

FutureProgrammers

LearnToCode

CodeWithPassion

CodingGoals

EmpowerWithCode

DigitalSkills

CodingExcellence

TechSavvy

CodeYourDreams

CodingForKids

CodeYourFuture

CodingPro

OnlineTutoring

TechEducationForAll

Join the Learn Codes family today and take the first step towards mastering the language of the future! 🌐

Enroll now: Contact Us Now at +91-7544842581 🌐 Visit our website: https://learncodes.co.in/

[dotnetinstitute]

Top 10 Reasons People are Choosing to Learn Computer Programming

The 21st century has seen rapid growth in technological development.

.

.

.

Link:- https://dnce.in/reasons-people-are-choosing-to-learn.../

.

.

.

#reasonstolearnprogramming

#whylearnprogramming

#programmingforbeginners

#learntocode

#codingforlife

#codingisfun

#codingiscool

#careerinprogramming

#FutureOfProgramming

#dotnetinstitute

[techpaathshala]

Coding equips you with valuable skills that can enhance your problem-solving abilities, career prospects, and understanding of technology. Additionally, if you work in an analytical sector, coding might improve your data-handling skills. Think about your areas of strength and development. It opens up a world of possibilities and provides a foundation for continuous learning and personal growth in an increasingly digital world.

Develop these skills at Techpaathshala! #techpaathshala #codingforlife #learnandgrow #careersinIT

[testing-gb]

Group Blog Posting Test

[paracoders-blog]

Like and follow for more amazing quiz..! . . . #paracoders . . . . . #codingquiz #codingchallenge #quizzes #pythonlearning #ballpythonlover #programminglibrarian #like4likess #follow #updates #programmingmemes #leadership #learntocode #codeblack #codingislife #learnsomethingnew #followfollowfollow #liketoknowit #lovecoding #codingforlife https://www.instagram.com/p/B-pIimRgP9s/?igshid=4cetbmq6034s

[alacraneando]

#arduino #raspberrypi #makers #lab #coding #codingforlife #hack #hacking #hacker #bidouille #numerique #freecode #picoftheday #color🔝🔝🔝🔝 #freedrive #openaccess #cc

[puttoo21]

Another Program With Python

Data management will need your own variables that will depend on the variables that you've selected and the decisions you made about them.Data management is a part of the research process, that you can and will return to again, and again, as you learn more, and are able to make better decisions.

In [1]:

import pandas as pd import numpy as np import os import matplotlib.pyplot as plt import seaborn

In [2]:

#this function reads data from csv file def read_data():    data = pd.read_csv('/home/data-sci/Desktop/analysis/course/nesarc_pds.csv',low_memory=False)                              return data

In [3]:

#this function saves the data in a pickle "binary" file so it's faster to deal with it next time we run the script def pickle_data(data):    data.to_pickle('cleaned_data.pickle') #this function reads data from the binary .pickle file def get_pickle():    return pd.read_pickle('cleaned_data.pickle')

In [4]:

def the_data():    """this function will check and read the data from the pickle file if not fond    it will read the csv file then pickle it"""    if os.path.isfile('cleaned_data.pickle'):        data = get_pickle()    else:        data = read_data()        pickle_data(data)    return data

In [20]:

data = the_data()

In [21]:

data.shape

Out[21]:

(43093, 3008)

In [22]:

data.head()

Out[22]:

In [102]:

data2 = data[['MARITAL','S1Q4A','AGE','S1Q4B','S1Q6A']] data2 = data2.rename(columns={'MARITAL':'marital','S1Q4A':'age_1st_mar',                      'AGE':'age','S1Q4B':'how_mar_ended','S1Q6A':'edu'})

In [103]:

#selecting the wanted range of values    #THE RANGE OF WANTED AGES data2['age'] = data2[data2['age'] < 30]    #THE RANGE OF WANTED AGES OF FISRT MARRIEGE #convert to numeric so we can subset the values < 25 data2['age_1st_mar'] = pd.to_numeric(data2['age_1st_mar'], errors='ignor')

In [105]:

data2 = data2[data2['age_1st_mar'] < 25 ] data2.age_1st_mar.value_counts()

Out[105]:

21.0    3473 19.0    2999 18.0    2944 20.0    2889 22.0    2652 23.0    2427 24.0    2071 17.0    1249 16.0     758 15.0     304 14.0     150 Name: age_1st_mar, dtype: int64

for simplicity will remap the variable ed to have just 4 levels

below high school education == 0

high school == 1

collage == 2

higher == 3

In [106]:

edu_remap ={1:0,2:0,3:0,4:0,5:0,6:0,7:0,8:1,9:1,10:1,11:1,12:2,13:2,14:3} data2['edu'] = data2['edu'].map(edu_remap)

Print the frequency of the values

In [107]:

def distribution(var_data):    """this function will print out the frequency    distribution for every variable in the data-frame   """    #var_data = pd.to_numeric(var_data, errors='ignore')    print("the count of the values in {}".format(var_data.name))    print(var_data.value_counts())    print("the % of every value in the {} variable  ".format(var_data.name))    print(var_data.value_counts(normalize=True))    print("-----------------------------------") def print_dist():    # this function loops though the variables and print them out    for i in data2.columns:        print(distribution(data2[i])) print_dist()

the count of the values in marital 1    13611 4     3793 3     3183 5      977 2      352 Name: marital, dtype: int64 the % of every value in the marital variable   1    0.621053 4    0.173070 3    0.145236 5    0.044579 2    0.016061 Name: marital, dtype: float64 ----------------------------------- None the count of the values in age_1st_mar 21.0    3473 19.0    2999 18.0    2944 20.0    2889 22.0    2652 23.0    2427 24.0    2071 17.0    1249 16.0     758 15.0     304 14.0     150 Name: age_1st_mar, dtype: int64 the % of every value in the age_1st_mar variable   21.0    0.158469 19.0    0.136841 18.0    0.134331 20.0    0.131822 22.0    0.121007 23.0    0.110741 24.0    0.094497 17.0    0.056990 16.0    0.034587 15.0    0.013871 14.0    0.006844 Name: age_1st_mar, dtype: float64 ----------------------------------- None the count of the values in age 1.0    1957 4.0     207 5.0     153 2.0      40 3.0       7 Name: age, dtype: int64 the % of every value in the age variable   1.0    0.827834 4.0    0.087563 5.0    0.064721 2.0    0.016920 3.0    0.002961 Name: age, dtype: float64 ----------------------------------- None the count of the values in how_mar_ended     10459 2     8361 1     2933 3      154 9        9 Name: how_mar_ended, dtype: int64 the % of every value in the how_mar_ended variable       0.477231 2    0.381502 1    0.133829 3    0.007027 9    0.000411 Name: how_mar_ended, dtype: float64 ----------------------------------- None the count of the values in edu 1    13491 0     4527 2     2688 3     1210 Name: edu, dtype: int64 the % of every value in the edu variable   1    0.615578 0    0.206561 2    0.122650 3    0.055211 Name: edu, dtype: float64 ----------------------------------- None

Summary

In [1]:

# ##### marital status                 # Married                0.48 %        | # Living with someone    0.22 %        | # Widowed                0.12 %        | # Divorced               0.1  %        | # Separated              0.03 %        | # Never Married          0.03 %        | #                                      | # -------------------------------------| # -------------------------------------| #                                      | # ##### AGE AT FIRST MARRIAGE FOR THOSE # WHO MARRY UNDER THE AGE OF 25        | # AGE     %                            | # 21    0.15 %                         | # 19    0.13 %                         | # 18    0.13 %                         | # 20    0.13 %                         | # 22    0.12 %                         | # 23    0.11 %                         | # 24    0.09 %                         | # 17    0.05 %                         | # 16    0.03 %                         | # 15    0.01 %                         | # 14    0.00 %                         | #                                      | # -------------------------------------| # -------------------------------------| #                                      | # ##### HOW FIRST MARRIAGE ENDED         # Widowed     0.65 %                   | # Divorced    0.25 %                   | # Other       0.09 %                   | # Unknown     0.004%                   | # Na          0.002%                   | #                                      | # -------------------------------------| # -------------------------------------| #                                      | # ##### education                       # high school               0.58 %     | # lower than high school    0.18 %     | # collage                   0.15 %     | # ms and higher             0.07 %     | #                                      |

1- Re-coding unknown values

from the variable "how_mar_ended" HOW FIRST MARRIAGE ENDED will code the 9 value from Unknown to NaN

In [13]:

data2['how_mar_ended'] = data2['how_mar_ended'].replace(9, np.nan) data2['age_1st_mar'] = data2['age_1st_mar'].replace(99, np.nan)

In [14]:

data2['how_mar_ended'].value_counts(sort=False, dropna=False)

Out[14]:

1     4025 9       98 3      201 2    10803     27966 Name: how_mar_ended, dtype: int64

In [23]:

#pickle the data tp binary .pickle file pickle_data(data2)

[discoveringcode]

The key to performance is elegance, not battalions of special cases. -Jon Bentley and Doug McIlroy coding #codinglife #codingbootcamp #codinglove #codingforlife #codingquotes #codingisthenewliteracy #programming #programminglife #programmingquotes #programmingwithapurpose #learning #learningbydoing #learninglife #learningtocode #learntocode #discover #discoveringcode (at New York, New York)

[dreams-yeah-unlimited-blog]

Super excited for my first home #automation project. #raspberrypi #NodeJS #codingforlife

[efectiq]

⚡ The code is poetry.⠀ ⠀ ✅ Check the link in bio!⠀ ⠀ Be educated. Be EFECTIQ. ⠀ #efectiq #beefectiq #CostaRica⠀ .⠀ #coding #codingtshirt #codingatschools #codingismycardio #codingquotes #codingdiva #codingeveryday #codingblonde #codingislove #codingfordays #codingtoys #codingame #codingmonkey #codingforlife #codingview #codingstyle #codingbaby #codingallday #codingshirts #codingIsLikeWorshipping #codingintheclassroom #codingjokes #Coding365 #codingforemails #codingquote #codingdad #codingblog — view on Instagram https://ift.tt/2XuIhHF

[innovatribe]

RT @EdSurge: How Scratch & Minecraft Developers Hope to Keep Kids Coding For Life: https://t.co/TMwKGvr44q @hourofcode… https://t.co/vtAvdtCaWE

How Scratch & Minecraft Developers Hope to Keep Kids Coding For Life: https://t.co/TMwKGvr44q @hourofcode #stemchat #edchat #codingforlife http://pic.twitter.com/aowVy5FJcL

— EdSurge (@EdSurge) July 7, 2017

from Twitter https://twitter.com/soozietwits July 07, 2017 at 09:26PM via IFTTT

[discoveringcode]

The way to tell a great idea is that, when people hear it, they say "Gee, I could have thought of that." -Richard Feynman coding #codinglife #codingbootcamp #codinglove #codingforlife #codingquotes #codingisthenewliteracy #programming #programminglife #programmingquotes #programmingwithapurpose #learning #learningbydoing #learninglife #learningtocode #learntocode #discover #discoveringcode (at New York, New York)

[discoveringcode]

Don't fear moving slowly. Fear standing still. -Chinese Proverb coding #codinglife #codingbootcamp #codinglove #codingforlife #codingquotes #codingisthenewliteracy #programming #programminglife #programmingquotes #programmingwithapurpose #learning #learningbydoing #learninglife #learningtocode #learntocode #discover #discoveringcode (at New York, New York)

[discoveringcode]

Success is easy to handle: You've solved the wrong problem. Work harder to improve. -Alan Perlis coding #codinglife #codingbootcamp #codinglove #codingforlife #codingquotes #codingisthenewliteracy #programming #programminglife #programmingquotes #programmingwithapurpose #learning #learningbydoing #learninglife #learningtocode #learntocode #discover #discoveringcode (at New York, New York)

[discoveringcode]

Premature optimization is the root of all evil in programming. -Donald Knuth. coding #codinglife #codingbootcamp #codinglove #codingforlife #codingquotes #codingisthenewliteracy #programming #programminglife #programmingquotes #programmingwithapurpose #learning #learningbydoing #learninglife #learningtocode #learntocode #discover #discoveringcode (at New York, New York)

[discoveringcode]

The best way to predict the future is to invent it. -Alan Kay #coding #codinglife #codingbootcamp #codinglove #codingforlife #codingquotes #codingisthenewliteracy #programming #programminglife #programmingquotes #programmingwithapurpose #learning #learningbydoing #learninglife #learningtocode #learntocode #discover #discoveringcode (at New York, New York)