Python Comprehensive Final & Tableau

F-string

print (f “I have this many {variable}.”)

String concatenation

print (“I understand how to + variable + fish at a specific temperature of + variable2 + “.” ”)

Combines strings with an operator like + (plus) , (comma) % (remainder)

if statements

allows you to make decisions on certain conditions through programming

if age >= 21:

print (“You can have a drink!”)

elif

else if; takes consideration of other decisions or numbers

if age == 21:

print (“You can have a drink!”)

elif age > 21:

print(“Oh yeah! You can definitely have a drink!”)

else

means that if all conditions were not met or not true; this considers all other factors that are not true

if age == 21:

print (“You can have a drink!”)

elif age > 21:

print (“Oh yeah! You can have a drink!”)

else:

print (“Have a glass of water.”)

+

addition

-

subtraction

*

multiplication

/

division

**

power

%

remainder

cast

when you take a function and cover it with another function outward

int(input(“What is your favorite number?”))

.upper()

make it upper case; CHANGE is PERMANENT

.lower()

CHANGE the name to lower case; no permanent

.title()

updates the name in proper form

.replace()

replaces values or update the values

.replace(“I”, ““)

.partition()

divides the strings into 3 separate parts based on a specific separator

name = “Franklin D. Roosevelt”

separateNames = name.replace()

output:

(“Franklin”, ““ “D. Roosevelt”)

.split()

splits a string into a list of strings after breaking the given string by a specified separator

name = "roseanne chaeyoung park”

separateName = name.split()

output

[“roseanne” “chaeyoung” “park”]

.strip()

strips unnecessary space

index

counts from zero to how many digits a character signifies

CHEESE = 0, 1, 2, 3, 4, 5 (in total would be 6 digits)

Tuples

Purpose: Collection of values

Special/Unique: cannot be modified; only read

Defined by: () —> parentheses

name_tuple = (“Jinsoul”, ““, “Kim”) —> index of 3

Loop

repeats the same action or sequence multiple times; requires an iterator/counter;

for each_name in name_tuple

print(each_name) - indented

print(“Stan Loona”) - indented

print(“Loop by Yves SOTY”)

List

Purpose: collection of values

Special: can be CHANGED

Defined: Squared Brackets []

.append()

Special to lists; adds to a list as a new value

.merge()

Given if there is another list to add to another —> list2.merge(list1)

.pop()

uses an index to remove something —> list1.pop(4) — pops the value holder of 4

.clear()

removes the list in its entirety

Sets

Purpose: collection of values/data

Unique: Does not allow for duplicates/all values have to be unique

Defined by: {} curly braces

.add()

Special to sets

adds another value to the set; but has to be a different value

.union()

two lists are asked to be combined; it does combine them and any duplicates are listed as 1

.intersection()

finds the values that are shared within both sets; join two sets but only includes common elements

.issubset()

the set is contained inside of another (mostly used in a if statement)

.issuperset()

contains another set in another (mostly use in a if statement)

Dictionaries

Purpose: A collection of values that are logically connected

Unique: DEFINED & MULTIPLE keys; each data point has a key/value pairing

Concept: Object-Oriented Programming

Function

A collection of “code” that can be “CALLED” multiple times from various points in our code

Parameter

passed in or returned out from a function

Argument

known or pre-defined parameter

PANDAs

Purpose: multidimensional table (2d)

series

one column of data; multiple series — creates a table

data frame

2 dimensional data (df)

import pandas as pd

is not necessary completely, but required if you want to use the features of PANDAs then you must do this

.Series()

makes a list of data into a series

df.head()

the first 5 rows are shown

df.tail()

the last 5 rows are shown

df.info()

when we want basic info about our df (how many rows, how many columns, what is the type of data)

pd.read_csv()

loads data directly into a dataframe

df.to_string()

outputs the df to a string

df.shape

how many rows and columns do we have in our df

.unique()

returns a list of all the unique values of columns

len(variable)

tells you how many in that list —> gives you a number

.isna()

how many rows in the columns have null values; returns a list of booleans

TRUE YES 1

FALSE NO 0

variable.sum()

counts how many in the list that we want to see that might have null values

.max()

gives the maximum value of a column

.min()

gives the minimum value of a column

.mean()

gives you the average of the column

.query()

looks through that section and gives you the specific row (subset) back to you either in another variable

columns/series need to be in backquotes

df.query(“ Starting Median Salary` > 75000”)

df.describe()

looks at all the descriptive statistics of the df

df.drop

drops the row in its entirety but there is some caution either in updating it or removing it in its entirety which switches its space

df.drop(index = 256, inplace = True) —> keeps it in place and just updates it

df.drop(index = 256) —> removes it completely and restructures the df

pd.to_numeric

df['Starting Median Salary'] = pd.to_numeric(df['Starting Median Salary'], errors='coerce')

takes the entire section and forces (“coerces it”) into numeric values

df.loc[21, 'Starting Median Salary'] = None
df.loc[22, 'Starting Median Salary'] = ""
df.loc[22, 'Starting Median Salary'] = 42600

used for updating a location by providing its index and column name, to which the person would add the value to “update it”

df.to_csv('/drive/My Drive/testfile.csv')  

to transfer the df into a csv file (comma separate value)

df['short_description'].replace(to_replace=r'^B',value='T', regex=True, inplace=True)

looks for patterns in data and completely updates with the regular expression value called “regex”