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130+ Exercises - Python Programming - Data Science - Pandas

Improve your Python programming and data science skills and solve over 130 exercises in Pandas!

Provided by e-smartdata

Summary

Price
£12 inc VAT
Study method
Online, On Demand What's this?
Duration
4.5 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed courses certificate of completion - Free
Additional info
  • Tutor is available to students

Overview

Welcome to the course 130+ Exercises - Python Programming - Data Science - Pandas where you can test your Python programming skills in data science, specifically in Pandas.

Certificates

Reed courses certificate of completion

Digital certificate - Included

Will be downloadable when all lectures have been completed

Curriculum

18
sections
269
lectures
4h 29m
total

    • 2: A few words from the author 01:00

    • 3: Exercise 0 01:00
    • 4: Solution 0 01:00

    • 5: Exercise 1 Preview 01:00
    • 6: Solution 1 Preview 01:00
    • 7: Exercise 2 01:00
    • 8: Solution 2 01:00
    • 9: Exercise 3 01:00
    • 10: Solution 3 01:00
    • 11: Exercise 4 01:00
    • 12: Solution 4 01:00
    • 13: Exercise 5 01:00
    • 14: Solution 5 01:00
    • 15: Exercise 6 01:00
    • 16: Solution 6 01:00
    • 17: Exercise 7 01:00
    • 18: Solution 7 01:00
    • 19: Exercise 8 01:00
    • 20: Solution 8 01:00
    • 21: Exercise 9 01:00
    • 22: Solution 9 01:00
    • 23: Exercise 10 01:00
    • 24: Solution 10 01:00

    • 25: Exercise 11 Preview 01:00
    • 26: Solution 11 Preview 01:00
    • 27: Exercise 12 01:00
    • 28: Solution 12 01:00
    • 29: Exercise 13 01:00
    • 30: Solution 13 01:00
    • 31: Exercise 14 01:00
    • 32: Solution 14 01:00
    • 33: Exercise 15 01:00
    • 34: Solution 15 01:00
    • 35: Exercise 16 01:00
    • 36: Solution 16 01:00
    • 37: Exercise 17 01:00
    • 38: Solution 17 01:00
    • 39: Exercise 18 01:00
    • 40: Solution 18 01:00
    • 41: Exercise 19 01:00
    • 42: Solution 19 01:00
    • 43: Exercise 20 01:00
    • 44: Solution 20 01:00

    • 45: Exercise 21 Preview 01:00
    • 46: Solution 21 Preview 01:00
    • 47: Exercise 22 01:00
    • 48: Solution 22 01:00
    • 49: Exercise 23 01:00
    • 50: Solution 23 01:00
    • 51: Exercise 24 01:00
    • 52: Solution 24 01:00
    • 53: Exercise 25 01:00
    • 54: Solution 25 01:00
    • 55: Exercise 26 01:00
    • 56: Solution 26 01:00
    • 57: Exercise 27 01:00
    • 58: Solution 27 01:00
    • 59: Exercise 28 01:00
    • 60: Solution 28 01:00
    • 61: Exercise 29 01:00
    • 62: Solution 29 01:00
    • 63: Exercise 30 01:00
    • 64: Solution 30 01:00

    • 65: Exercise 31 Preview 01:00
    • 66: Solution 31 Preview 01:00
    • 67: Exercise 32 01:00
    • 68: Solution 32 01:00
    • 69: Exercise 33 01:00
    • 70: Solution 33 01:00
    • 71: Exercise 34 01:00
    • 72: Solution 34 01:00
    • 73: Exercise 35 01:00
    • 74: Solution 35 01:00
    • 75: Exercise 36 01:00
    • 76: Solution 36 01:00
    • 77: Exercise 37 01:00
    • 78: Solution 37 01:00
    • 79: Exercise 38 01:00
    • 80: Solution 38 01:00
    • 81: Exercise 39 01:00
    • 82: Solution 39 01:00
    • 83: Exercise 40 01:00
    • 84: Solution 40 01:00

    • 85: Exercise 41 Preview 01:00
    • 86: Solution 41 Preview 01:00
    • 87: Exercise 42 01:00
    • 88: Solution 42 01:00
    • 89: Exercise 43 01:00
    • 90: Solution 43 01:00
    • 91: Exercise 44 01:00
    • 92: Solution 44 01:00
    • 93: Exercise 45 01:00
    • 94: Solution 45 01:00
    • 95: Exercise 46 01:00
    • 96: Solution 46 01:00
    • 97: Exercise 47 01:00
    • 98: Solution 47 01:00
    • 99: Exercise 48 01:00
    • 100: Solution 48 01:00
    • 101: Exercise 49 01:00
    • 102: Solution 49 01:00
    • 103: Exercise 50 01:00
    • 104: Solution 50 01:00

    • 105: Exercise 51 01:00
    • 106: Solution 51 01:00
    • 107: Exercise 52 01:00
    • 108: Solution 52 01:00
    • 109: Exercise 53 01:00
    • 110: Solution 53 01:00
    • 111: Exercise 54 01:00
    • 112: Solution 54 01:00
    • 113: Exercise 55 01:00
    • 114: Solution 55 01:00
    • 115: Exercise 56 01:00
    • 116: Solution 56 01:00
    • 117: Exercise 57 01:00
    • 118: Solution 57 01:00
    • 119: Exercise 58 01:00
    • 120: Solution 58 01:00
    • 121: Exercise 59 01:00
    • 122: Solution 59 01:00
    • 123: Exercise 60 01:00
    • 124: Solution 60 01:00

    • 125: Exercise 61 01:00
    • 126: Solution 61 01:00
    • 127: Exercise 62 01:00
    • 128: Solution 62 01:00
    • 129: Exercise 63 01:00
    • 130: Solution 63 01:00
    • 131: Exercise 64 01:00
    • 132: Solution 64 01:00
    • 133: Exercise 65 01:00
    • 134: Solution 65 01:00
    • 135: Exercise 66 01:00
    • 136: Solution 66 01:00
    • 137: Exercise 67 01:00
    • 138: Solution 67 01:00
    • 139: Exercise 68 01:00
    • 140: Solution 68 01:00
    • 141: Exercise 69 01:00
    • 142: Solution 69 01:00
    • 143: Exercise 70 01:00
    • 144: Solution 70 01:00

    • 145: Exercise 71 01:00
    • 146: Solution 71 01:00
    • 147: Exercise 72 01:00
    • 148: Solution 72 01:00
    • 149: Exercise 73 01:00
    • 150: Solution 73 01:00
    • 151: Exercise 74 01:00
    • 152: Solution 74 01:00
    • 153: Exercise 75 01:00
    • 154: Solution 75 01:00
    • 155: Exercise 76 01:00
    • 156: Solution 76 01:00
    • 157: Exercise 77 01:00
    • 158: Solution 77 01:00
    • 159: Exercise 78 01:00
    • 160: Solution 78 01:00
    • 161: Exercise 79 01:00
    • 162: Solution 79 01:00
    • 163: Exercise 80 01:00
    • 164: Solution 80 01:00

    • 165: Exercise 81 01:00
    • 166: Solution 81 01:00
    • 167: Exercise 82 01:00
    • 168: Solution 82 01:00
    • 169: Exercise 83 01:00
    • 170: Solution 83 01:00
    • 171: Exercise 84 01:00
    • 172: Solution 84 01:00
    • 173: Exercise 85 01:00
    • 174: Solution 85 01:00
    • 175: Exercise 86 01:00
    • 176: Solution 86 01:00
    • 177: Exercise 87 01:00
    • 178: Solution 87 01:00
    • 179: Exercise 88 01:00
    • 180: Solution 88 01:00
    • 181: Exercise 89 01:00
    • 182: Solution 89 01:00
    • 183: Exercise 90 01:00
    • 184: Solution 90 01:00

    • 185: Exercise 91 01:00
    • 186: Solution 91 01:00
    • 187: Exercise 92 01:00
    • 188: Solution 92 01:00
    • 189: Exercise 93 01:00
    • 190: Solution 93 01:00
    • 191: Exercise 94 01:00
    • 192: Solution 94 01:00
    • 193: Exercise 95 01:00
    • 194: Solution 95 01:00
    • 195: Exercise 96 01:00
    • 196: Solution 96 01:00
    • 197: Exercise 97 01:00
    • 198: Solution 97 01:00
    • 199: Exercise 98 01:00
    • 200: Solution 98 01:00
    • 201: Exercise 99 01:00
    • 202: Solution 99 01:00
    • 203: Exercise 100 01:00
    • 204: Solution 100 01:00

    • 205: Exercise 101 01:00
    • 206: Solution 101 01:00
    • 207: Exercise 102 01:00
    • 208: Solution 102 01:00
    • 209: Exercise 103 01:00
    • 210: Solution 103 01:00
    • 211: Exercise 104 01:00
    • 212: Solution 104 01:00
    • 213: Exercise 105 01:00
    • 214: Solution 105 01:00
    • 215: Exercise 106 01:00
    • 216: Solution 106 01:00
    • 217: Exercise 107 01:00
    • 218: Solution 107 01:00
    • 219: Exercise 108 01:00
    • 220: Solution 108 01:00
    • 221: Exercise 109 01:00
    • 222: Solution 109 01:00
    • 223: Exercise 110 01:00
    • 224: Solution 110 01:00

    • 225: Exercise 111 01:00
    • 226: Solution 111 01:00
    • 227: Exercise 112 01:00
    • 228: Solution 112 01:00
    • 229: Exercise 113 01:00
    • 230: Solution 113 01:00
    • 231: Exercise 114 01:00
    • 232: Solution 114 01:00
    • 233: Exercise 115 01:00
    • 234: Solution 115 01:00
    • 235: Exercise 116 01:00
    • 236: Solution 116 01:00
    • 237: Exercise 117 01:00
    • 238: Solution 117 01:00
    • 239: Exercise 118 01:00
    • 240: Solution 118 01:00
    • 241: Exercise 119 01:00
    • 242: Solution 119 01:00
    • 243: Exercise 120 01:00
    • 244: Solution 120 01:00

    • 245: Exercise 121 01:00
    • 246: Solution 121 01:00
    • 247: Exercise 122 01:00
    • 248: Solution 122 01:00
    • 249: Exercise 123 01:00
    • 250: Solution 123 01:00
    • 251: Exercise 124 01:00
    • 252: Solution 124 01:00
    • 253: Exercise 125 01:00
    • 254: Solution 125 01:00
    • 255: Exercise 126 01:00
    • 256: Solution 126 01:00
    • 257: Exercise 127 01:00
    • 258: Solution 127 01:00
    • 259: Exercise 128 01:00
    • 260: Solution 128 01:00
    • 261: Exercise 129 01:00
    • 262: Solution 129 01:00
    • 263: Exercise 130 01:00
    • 264: Solution 130 01:00

    • 265: Exercise 131 01:00
    • 266: Solution 131 01:00
    • 267: Exercise 132 01:00
    • 268: Solution 132 01:00

    • 269: Facebook Group 01:00

Course media

Description

---------------------------------------

RECOMMENDED LEARNING PATH

---------------------------------------

PYTHON DEVELOPER:

  • 200+ Exercises - Programming in Python - from A to Z

  • 210+ Exercises - Python Standard Libraries - from A to Z

  • 150+ Exercises - Object Oriented Programming in Python - OOP

  • 150+ Exercises - Data Structures in Python - Hands-On

  • 100+ Exercises - Advanced Python Programming

  • 100+ Exercises - Unit tests in Python - unittest framework

  • 100+ Exercises - Python Programming - Data Science - NumPy

  • 100+ Exercises - Python Programming - Data Science - Pandas

  • 100+ Exercises - Python - Data Science - scikit-learn

  • 250+ Exercises - Data Science Bootcamp in Python

  • 110+ Exercises - Python + SQL (sqlite3) - SQLite Databases

  • 250+ Questions - Job Interview - Python Developer

SQL DEVELOPER:

  • SQL Bootcamp - Hands-On Exercises - SQLite - Part I

  • SQL Bootcamp - Hands-On Exercises - SQLite - Part II

  • 110+ Exercises - Python + SQL (sqlite3) - SQLite Databases

-----------------

DESCRIPTION

-----------------

Some topics you will find in the exercises:

  • working with Series

  • working with DatetimeIndex

  • working with DataFrames

  • reading/writing files

  • working with different data types in DataFrames

  • working with indexes

  • working with missing values

  • filtering data

  • sorting data

  • grouping data

  • mapping columns

  • computing correlation

  • concatenating DataFrames

  • calculating cumulative statistics

  • working with duplicate values

  • preparing data to machine learning models

  • dummy encoding

  • working with csv and json filles

  • merging DataFrames

  • pivot tables

The course is designed for people who have basic knowledge in Python, NumPy and Pandas. It consists of 130 exercises with solutions.

This is a great test for people who are learning the Python language and data science and are looking for new challenges. Exercises are also a good test before the interview. Many popular topics were covered in this course.

If you're wondering if it's worth taking a step towards Python, don't hesitate any longer and take the challenge today.

Who is this course for?

  • everyone who wants to learn by doing
  • everyone who wants to improve their Python programming skills
  • everyone who wants to improve their data science skills
  • everyone who wants to prepare for an interview

Requirements

  • completed course '200+ Exercises - Programming in Python - from A to Z'
  • completed course '210+ Exercises - Python Standard Libraries - from A to Z'
  • completed course '150+ Exercises - Object Oriented Programming in Python - OOP'
  • completed course '100+ Exercises - Python Programming - Data Science - NumPy'
  • basic knowledge of Pandas

Career path

  • Data Scientist/Data Analyst/Machine Learning Engineer

Questions and answers

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Reviews

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Provider

e-smartdata

Python Developer/Data Scientist/Stockbroker

Founder at e-smartdata[.]org.

View e-smartdata profile

Legal information

This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
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