Data Science code that appears all the time at workplace

Learn exactly all the Programming (Python) skills that are needed all the time at workplace. Each video = 1 skill.
Data Science code that appears all the time at workplace
File Size :
4.84 GB
Total length :
12h 37m



Dr. Giannelos


Last update




Data Science code that appears all the time at workplace

What you’ll learn

I have issued a discount coupon for this course. It is on my website. Visit www g-algorithms com
In practice, 1% of Python is used 99% of the time. This course focuses on this 1%.
Great for Quants/ Economists, Data Scientists/ Software Engineers: the skills shown here, come up all the time.
This is your Help Resource when you are under heavy pressure! You open this course and search for a keyword/command you want. And you will watch the video.
You will not need to google-search to find answers all the time. Just have this course open in one tab of your browser so that any time you search in it!
The subtitles are manually created. Therefore, they are fully accurate. They are not auto-generated.
Hi ! To get this course at a lower cost do this: visit www iqfec com and at the bottom right corner send a message and I will send you a link

Data Science code that appears all the time at workplace


There are no prerequisites because we build all the necessary knowledge slowly! Jump straight in!


What is the course about:This online course focuses on the part of Data Science that keeps appearing all the time in any workplace. Save time and learn only what you will need 99.9% of the time. The idea is that this course can be your encyclopedia. When you don’t remember how something is done on Python, you just come to this course and search for the keyword and immediately you watch the video and remind yourself. Python and Data Science are like an ocean; you can keep learning and learning forever! But In the end, at work, you will need to perform, as quickly as possible. And this comes down to knowing the skills, the techniques, taught in this course! ​There are no prerequisites. Every topic is analyzed in depth so you can feel confident about what you learn. Every video is a building block. Once you know these building blocks you can do anything with data science. Who:Dr. Giannelos is a Research Scientist at Imperial College London, and has been part of high-tech projects at the intersection of Academia & Industry, prior to, during & after his Ph.D. Doctor of Philosophy (Ph.D.) in Analytics & Mathematical Optimization applied to Energy Investments, from Imperial College London, and Masters of Engineering (M. Eng.) in Power Systems and Economics. Important:Prerequisites: Nothing is needed. Simply start learning by watching the videos. You will immediately feel confident with Python.Every detail is explained, so that you won’t have to search online, or guess.  We start from scratch so that you do not need to have done any preparatory work in advance at all.  Just follow what is shown on screen, because we go slowly and look at everything in detail.


Section 1: Introduction

Lecture 1 Summary

Section 2: Installing the necessary software

Lecture 2 Install Python

Section 3: Interacting with data in external sources

Lecture 3 How to effectively read an xlsx file

Lecture 4 How to skip reading some rows when reading a dataframe

Lecture 5 How to effectively read a specific sheet from an excel file into a dataframe

Lecture 6 How to set the index of a dataframe upon reading it

Lecture 7 How to read specific columns from an excel file into a dataframe (usecols)

Lecture 8 How to read data from World Bank’s online database

Lecture 9 How to send many dataframes into the same excel file (xlsx) in different sheets

Lecture 10 Effectively sending a dataframe to a csv file

Lecture 11 How to hide warnings that Python produces. And how to trigger manually warnings

Lecture 12 How to read only some rows from the top/bottom of a dataframe(nrows, skipfooter)

Lecture 13 How to check if an Excel cell is empty

Lecture 14 How to see the version of the packages we have installed

Section 4: Index of a dataframe

Lecture 15 Columns becoming the index and vice versa: reset_index, set_index, drop=T

Lecture 16 How to change the index name of a dataframe

Lecture 17 How to find the row index & column index of any element of a dataframe

Lecture 18 How to enumerate the rows (enumerate) and use it in for loops

Lecture 19 How to sort the index of a dataframe (sort_index)

Section 5: Lists

Lecture 20 How to sort the elements of a list using lambda functions

Lecture 21 How to remove some elements from a list at once

Lecture 22 How to create a list (sublist) that has some elements of another list

Lecture 23 Defining a list with numbers 1,2,3,..,9 using list comprehension

Lecture 24 How to print the first 5 and the last 5 elements of a list

Lecture 25 How to include the elements of another list into a list (extend versus append)

Lecture 26 How to remove all occurrences of an element from a list

Lecture 27 Difference between pop() and remove()

Lecture 28 List comprehension

Lecture 29 Slicing

Lecture 30 Enumerate the list (enumerate, index)

Lecture 31 The command isin: series.isin(list)

Lecture 32 Count how many times an element is in a list (count)

Section 6: Dataframes at Workplace

Lecture 33 How to return elements from a dataframe

Lecture 34 How to delete rows/columns from a dataframe using iloc, drop

Lecture 35 How to read the row /column index and values (df.values)

Lecture 36 How to show the max number of rows and columns of a dataframe (set_option)

Lecture 37 How to create a copy of a dataframe

Lecture 38 How to correctly take a backup of a dataframe (copy() vs =)

Lecture 39 How to change specific values of a dataframe while leaving the rest unchanged

Lecture 40 Create a new column and populate with elements of another column of a dataframe

Lecture 41 How to change the order of the columns of a dataframe

Lecture 42 How to create a new row in a dataframe and fill it with values from other rows

Lecture 43 How to fill a new column with values 1,2,3,… (np.arange)

Lecture 44 How to use Pivot tables on Python

Lecture 45 How to rename rows and columns of a dataframe

Lecture 46 How to create a dataframe using a dictionary

Lecture 47 How to find the transpose of a dataframe

Lecture 48 Selecting rows and columns from a dataframe

Lecture 49 Copy-paste a row of a dataframe (np.repeat)

Lecture 50 How to sort the columns of a dataframe

Lecture 51 How to change the data type of a column / row of a dataframe

Lecture 52 How to select many rows (loc, arange)

Lecture 53 How to delete many rows from a dataframe at once

Lecture 54 How to return the value under other columns, in the same row of a dataframe

Lecture 55 How to Iterate through the rows of a dataframe iteritems

Lecture 56 How to sort the values of a column (sort_values() )

Lecture 57 How to populate a column using list, arrays, Series

Lecture 58 Format the values of a dataframe to percentages

Lecture 59 Define a dataframe with and without a dictionary

Section 7: Loops

Lecture 60 Prevent copying rows from a dataframe to dict (continue)

Lecture 61 How to not allow duplicate values while inserting a new row (next)

Lecture 62 Avoid duplicate entries using while and break

Lecture 63 continue, break, pass

Lecture 64 for – else

Lecture 65 while for equivalence

Lecture 66 While True

Lecture 67 While else

Section 8: multi – level (column) dataframes

Lecture 68 How to define a dataframe whose column index has many levels (headers)

Lecture 69 How to rename a column (rename)

Lecture 70 How to remove a level

Lecture 71 Compressing all levels into 1 excel cell or showing them as is (merge_cells)

Lecture 72 How to print dataframe merged cells as unmerged in excel (startrow)

Lecture 73 How to iterate through the rows of a multi level dataframe (iteritems)

Section 9: Conditionals

Lecture 74 if- elif statement

Lecture 75 Inline if statement

Section 10: Logicals

Lecture 76 How to correctly write AND OR TRUE FALSE

Lecture 77 How to correctly write the NOT operator

Lecture 78 The De Morgan’s Law. AND, OR, NOT, statements

Lecture 79 Comparing objects of type int, str, float, bool with each other

Lecture 80 Type conversions: Int, Float, Str, Bool

Lecture 81 Combining NOT with empty lists and strings

Lecture 82 what it means for x to be none, empty list, empty string

Section 11: Tuples

Lecture 83 How to iterate through tuples. Different types of for-loops

Lecture 84 How to concatenate 2 tuples

Lecture 85 Defining a tuple

Lecture 86 Sorting a tuple

Lecture 87 Enumerating a tuple (enumerate, index)

Lecture 88 Find the frequency of elements in a tuple (count)

Section 12: NaN values

Lecture 89 How to remove NaN values by deleting rows or columns (dropna)

Lecture 90 Find if a dataframe has at least 1 missing value. And find their exact location!

Lecture 91 Using min_count to sum if there are NaN values

Lecture 92 Manually place NaN values to dataframes

Lecture 93 Sum rows of a dataframe by ignoring NaN (skipna)

Lecture 94 Replace missing values with 0

Section 13: Python Implementation of Excel Functions

Lecture 95 Model the Vlookup Excel function on Python (map function)

Lecture 96 Model the SUMIFS function on Python

Lecture 97 Model the AVERAGEIFS function on Python

Section 14: Strings

Lecture 98 How to evaluate string expressions using eval ()

Lecture 99 Removing characters from end, start of a string (lstrip, rstrip)

Lecture 100 How to break a long sequence of characters in sets of characters (wrap)

Lecture 101 How to select part of a string (e.g. all string except last 3 characters)

Lecture 102 Use replace() to remove white spaces from a string.

Lecture 103 Find multiple occurrences of a subtext in a long string

Lecture 104 Selecting specific characters from a column using “str”

Lecture 105 How to replace text of a string using replace()

Lecture 106 Join strings from inside a list (join)

Lecture 107 multiline strings

Lecture 108 formatting strings and f-strings (format)

Lecture 109 Count how many times a character is in a string

Lecture 110 in, find() with strings

Lecture 111 Right justify text (rjust)

Section 15: Creating variables

Lecture 112 How to define variables using globals()

Lecture 113 Multiple assignment

Section 16: Sets

Lecture 114 Define a set, add/remove elements

Lecture 115 Convert a list string to a set

Lecture 116 Difference of two sets. Symmetric difference. Difference update

Lecture 117 Set comprehension

Lecture 118 Subset, superset, proper subset

Lecture 119 Intersection & union of two sets

Section 17: Series

Lecture 120 Editing strings inside series (series.str[])

Lecture 121 How to define a series object that has a constant value (pd.Series)

Lecture 122 Selecting a column as a Series object versus as a Dataframe

Lecture 123 Storing an array to a dataframe (broadcasting)

Section 18: Numpy Arrays

Lecture 124 How to concatenate arrays (append)

Lecture 125 How to create equally spaced numbers linspace

Lecture 126 Reshaping the arrays (reshape)

Lecture 127 1D 2D 3D arrays from lists

Lecture 128 How to modify elements of an array

Lecture 129 How to use arange to create 1D and 2D arrays

Lecture 130 eye ones zeros. Instantly make arrays of constants

Lecture 131 Flattening an array (collapsing it to 1D) (flatten() )

Section 19: Functions

Lecture 132 Docstring

Lecture 133 Count how many times a function is called

Lecture 134 How to return many values from a function

Lecture 135 Default values for parameters

Lecture 136 A function calling another function

Section 20: Dates

Lecture 137 How to update a value in a DateTime index in a dataframe.

Lecture 138 Using the Workalendar package for country-specific Dates

Lecture 139 Use timedelta() for time conversions

Section 21: Datatypes

Lecture 140 Use __name__ to find the datatype of an object

Lecture 141 How to check if the datatype of a variable is: int, float, str, NaN, Nonetype

Lecture 142 Datatype of every element of a dataframe: for loop, dtypes, astype()

Section 22: Dictionaries

Lecture 143 Define a dictionary and loop through it

Lecture 144 How to find the number of elements in a dictionary (len)

Lecture 145 Convert a dictionary into a list/set of keys/values. Find its unique values

Lecture 146 How to convert a dataframe to a dictionary (to_dict) and how to use it

Lecture 147 How to print the first 6 elements of a dictionary

Lecture 148 What it means to check if x is in dictionary

Lecture 149 How to convert a single value into a dictionary (all keys are this value)

Lecture 150 How to avoid errors when a key is not found in a dictionary (command: get)

Lecture 151 How to unite two dictionaries (double asterisk)

Lecture 152 Dictionary comprehension

Lecture 153 Delete a key from a dictionary

Lecture 154 Sort a dictionary

Section 23: Special types of dictionaries

Lecture 155 Default dictionary: how it works and why use it.

Section 24: Basic mathematics

Lecture 156 Trigonometry , infinite and pi

Lecture 157 regular/integer/modulo division // %

Lecture 158 dot product of two arrays

Section 25: Errors (Exceptions)

Lecture 159 How to manually trigger errors based on user input (raise ValueError)

Lecture 160 Deal with errors via the Try-Except block

Lecture 161 The statement “finally”

Section 26: Random package

Lecture 162 random choice

Lecture 163 randint

Lecture 164 randrange

Lecture 165 random.random, random.seed

Lecture 166 random.sample (sample without replacement)

Section 27: Bonus

Lecture 167 Extras

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Course Information:

Udemy | English | 12h 37m | 4.84 GB
Created by: Dr. Giannelos

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