Exam #3 Cheat Sheet (DRAFT) by ryurk

def hello():
       print("hello world")
invoke it with hello()

def welcome(name):
        print(f'Hello, {name}")
invoke it with welcom("amy"

def welcome_greeting(name, greeting)
        print(f' Hey{name}. {greeting}")
invoke it with welcome_greeting('Liz', "How are you?") --> Kwargs

def exponent(base,exponent):
       power = base ** exponent
       return power
num1 = 2
num2 = 3
answer = exponent(num1,num2)
print(answer)
print(exponent(2,3))

def sum(*parameters): --> unlimited parameters
        total = 0
for i in parameters:
        total = total + each_number
        return total
sum = sum(1,2,3,4,5)
print(sum)

Df = pd.read_csv(url)

print(df.to_string())

Null is null, a null is something = a special creation to indicate the absence of a value - its a made up value

Df.shape = not a method does not need ()

Look at only one column = df[‘School Name’]

print( df[ ‘School Name’ ].to_string( ))

Find the unique names= df[‘School Name’].unique()

type(unique_schools) shows the type.. This is not a data frame

Statistics:

df[‘Starting Salary’].max() or df[‘Starting Salary’].mean() or df[‘Starting Salary’].min()

Find the NAs = df[‘Starting Salary’].isna() then to count the trues = na_rows.sum()

Based on a condition

Df2 = df.query(“ 
Starting Salary
 > 75000 “)

Change individual values

Df.loc[20, ‘Starting Salary’] = ‘ ‘

Convert to numeric type = df[‘Starting Salary’] = pd.to_numeric( df[‘Starting Salary’] )

Df.loc[139, ‘Starting Salary’] = 46000


#Convert starting salary to numeric FORCE CONVERT or “COERCE” conversion

Error_columns = pd.to_numeric( df[‘Starting Salary’], errors= ‘coerce’)

print(error_columns)

#find the NAs

Nas = error_columns.isna()

print(Nas)

Df[20:25]

#fix columns

Df.loc[70, ‘Starting Salary’] = 42600

df[Nas]


Save it to the original by overwriting

df[‘Starting Salary’] = pd.to_numeric(df[‘Starting Salary’])

import pandas as pd
data_list = [45,74,78]
series_of_numbers = pd.series(data_list)
print(serties_of_numbers[1]) --> 74

Years = [2021, 2022, 2023]
series_of_numbers = pd.series(data=data_list, index=years)
print(series_of_numbers)
print(series_of_numbers[2021] --> 2021

grades = {'A': 34, 'B': 56}
grade_series = pd.series(data=grades)
print(grade_series) or print(grade_series['A']

quiz_scores = {
             'Quiz1': [32,56,56],
             'Quiz2': [78,34,32]}
df = pd.DataFrame(data=quiz_scores)
print(df)
# to overwrite -->
df = pd.DataFrame(data=quiz_scores. index=['mike', 'susan', 'amy']

df.head() --> top 5 rows
df.tail() --> bottom 5 rows
df[40:60] --> rows 40-59

Find all the schools with the name Pitt

Df2 = df.query( “ 
School Name
 == ‘Pitt’ “)

df2.head()

Remove a column: df.drop(columns=’Starting Salary’, inplace = True)

Or df = df.drop(columns=’Starting Salary’)

Drop a row df.drop(index=2, inplace = True)

Delete entire row of data when one column had missing data df=df.dropna()

Load descriptives for the df = df.describe()

Load tab-delimitted file

Df2 = pd.read_csv(URL, sep=’\t’ )

Replace function:

Df[ ‘School Name’].replace(‘-’, ‘ -’, regex=True, inplace=True)


Fillnas = df[‘Starting Salary’].fillna(0, inplace=True)


How many unique school names are there:

len( df[‘School Name’].unique())


Show only the rows in which df are duplicate:

Duplicates = df.duplicated(subset= ‘School Name’)

Boolean series = df[duplicates]

Df2 = df.drop_duplicates(subset=’School Name’, keep=’first’)


Find out schools with specified

PA_schools = df2[‘School Name’].str.contains(‘Pennsylvania’)

Use a boolean series df2[PA_schools]

Overwrite instead on inplace

Df2 = df2.sort_values(‘Starting Salary’, ascending = False)

Fix one bad value:

Df2.loc[2, ‘Starting Salary’] = df2[‘Starting Salary’].mean()