Essential Code for Data-Science Projects COMPLETE COURSE

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1. Introduction

1. Installing the necessary software

2. Interacting with data in external sources

1. 1. How to effectively read an xlsx file

2. Skip reading rows when reading a dataframe

3. How to read a specific sheet from an excel file

4. Set the index of a dataframe upon reading it

5. Read specific columns from an excel file

6. Read data from World Bank

7. Send many dataframes into the same Excel file

8. Send a dataframe to a CSV

9. Hide Warnings

10. Reading rows from topbottom (nrows, skipfooter)

11. Check if an Excel cell is empty

12. Check the version of the installed packages

13. Hide special warnings

3. Index of a dataframe

1. 1. Set and reset the index

2. Change the name of the index of a dataframe

3. Find the rowcolumn index of any element of a dataframe

4. The enumerate() command to enumerate rowselements

5. Sort the index of a dataframe sort index

4. Lists

1. How to sort the elements of a list

2. Remove elements from a list

3. Create a sublist from another list

4. List comprehension from consecutive numbers

5. Print first last elements of a list

6. Place in a list the elements of another list

7. Remove all occurrences of an element

8. pop() versus remove()

9. List comprehensions

10. Slicing

11. Enumerate, index

12. series.isin(list)

13. Count how many times an element is in a list

14. Make a copy and an alias of a list

5. Important Dataframe operations

1. Return elements from a dataframe

2. Delete rows columns (iloc, drop)

3. Read indices and values of a dataframe

4. Show the max number of rowscolumns

5. Create a copy of a dataframe

6. Take a backup of a dataframe (copy versus =)

7. Change specific values in a dataframe

8. Make a new column and fill it with the values of another column

9. Change the order of columns in a dataframe

10. New row and fill it with the values of other rows

11. New column with values 1,2,3... (arange)

12. pivot tables

13. Rename rows columns of a dataframe

14. Use a dictionary to create a dataframe

15. Transpose of a dataframe

16. Select rowscolumns of a dataframe

17. Repeat a rowcolumn (np.repeat)

18. Sorting the columns

19. Change the datatype of a rowcolumn (astype)

20. Select specific rowscolumns (loc, arange)

21. How to delete many rows from a dataframe

22. Get the value under another column of the same row

23. Use iteritems on a dataframe

24. Sort the values of a column (sort values)

25. Populate a column via list, array, series

26. Define a dataframe with without a dictionary

27. Define a dataframe using list comprehension

6. The Apply command

1. Format the values of a dataframe to percentages

2. Format the elements of a dataframe to 1 decimal point

3. Apply a function to the elements of a series dataframe

7. Loops

1. Prevent data transfer from dataframe to dictionary

2. Prevent duplicate values in a dataframe

3. Prevent duplicate values while using True, Break

4. Break, continue, pass

5. For, else

6. While, For equivalence

7. While True

8. While , Else

8. Multilevel (columns) dataframes

1. How to define it

2. How to rename a column

3. How to remove a level

4. Print levels in a single cell (merge cells)

5. From merged (dataframe) to unmerged (excel)

6. How to implement iteritems

9. Conditionals

1. If, elif

2. inline if statement

3. Print statement with if-else embedded

10. Logicals

1. AND, OR, FALSE, TRUE

2. NOT

3. The De Morgan Laws

4. Comparison of int, str, Float, Bool

5. Type conversions int , float, str, bool

6. Combining NOT with empty string lists

7. The meaning of x = None, [] ,

8. Difference between is and =

11. Tuples

1. Iterate via for-loop

2. Join two tuples

3. Define a tuple

4. Sort a tuple

5. Enumerate a tuple (enumerate, index)

6. Find the frequency of elements (count)

7. Tuple immutability

12. NaN values

1. Remove NaN values by deleting rowscolumns

2. How to find if a dataframe has NaN values

3. Use min count to sum in presence of NaN values

4. Place NaN values in a dataframe , manually

5. Sum rows by ignoring NaN (skipna)

6. Replace missing values with 0

13. Python Implementation of Excel functions

1. Modelling VLOOKUP in Python

2. Modelling SUMIFS in Python

3. Modelling AVERAGEIFS in Python

14. Strings

1. Evaluate String expressions using eval()

2. Remove trailing characters using rstrip, lstrip

3. Break a string in sets using wrap

4. Select part of a string

5. Remove white space using replace ()

6. Search for multiple occurrences of a subtext

7. Select specific characters from a column using str

8. Replace a character or a word from inside a string

9. Unite strings from inside a list using join

10. Multiline strings

11. Formating strings and f-strings (format)

12. Count how many times a character is inside a string

13. in, find() with strings

14. right-justify text using rjust

15. Creating variables

1. Using the function globals()

2. Multiple assignment

3. Use global to change variables

16. Sets

1. Define a set, addremove elements

2. Convert a liststring to a set

3. Difference of two sets symmetric difference

4. Set comprehension

5. Subset, superset, proper subsetsuperset

6. Intersection and union

17. Series

1. Edit strings inside series using. .str[]

2. Create a series object of a constant value

3. Select a column as Series versus as a Dataframe

4. Broadcasting (saving) an array to a Series

18. Numpy arrays

1. Concatenate two arrays

2. linspace (equally spaced numbers)

3. Reshaping the array

4. 1D, 2D, 3D arrays from lists

5. Modify elements of an array

6. Use arange to make 1D and 2D arrays

7. eye, ones, zeros

8. flattening an array

19. Functions

1. Docstring

2. Count how many times a function is called

3. How to return many values from a function

4. Default values for parameters

5. A function calling another function

20. Date objects

1. Update a DateTimeIndex

2. The Workalendar package for Country dates

3. Timedelta() for time conversions

21. Datatypes

1. Use name

2. Check if the datatype is int float str NaN None

3. dtypes, astype() and the type of the dataframe elements

4. Converting elements of a column via astype

22. Dictionaries

1. Define and loop through a dictionary

2. Find the number of elements in a dictionary

3. Convert to a listset of keysvalues

4. Convert a dataframe to a dictionary

5. Print the first 6 elements of a dictionary

6. What it means for x in dictionary

7. Convert a single value into a dictionary (keys)

8. Avoid errors when a key is not found (get)

9. Join two dictionaries ()

10. Dictionary comprehension

11. Delete a key from a dictionary

12. Sort a dictionary

13. Mutability of a dictionary

23. Special dictionaries

1. Default dictionaries

24. Maths

1. Trigonometry, infinity, pi

2. regular division, integer division, modulo division, , %

3. dot product of 2 arrays

25. Errors

1. Try Except block

2. the finally statement

3. Raise errors based on user input

4. How to raise own errors

26. The Random package

1. random choice

2. randint

3. randrange

4. random.random, random.seed

5. random.sample (sample without replacement)

6. Fix the random seeds using a function

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