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Data Science 1 exam


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Create List
List1 = [val1, val2, val3, …]
Print List
List1 = [val1, val2, val3, …] print (List1)
Change List Elements
List1[2] = “Lisa” print(List1) #It prints [val1, val2, “Lisa”, …]
Access Subjects in List
List1 = [val1, val2, “Lisa”, …] List1[1] # val2 print(List1)
Append List:
List1 = [val1, val2, “Lisa”, …] List1.append([v1, v2, v3]) # changes original, adds 1 value, a list
Extend List:
List1 = [val1, val2, “Lisa”, …] List1.extend([v1, v2, v3]) # changes original adds 3 values
Key list information
Heterogeneous e.g., mixed_string = [1, 2, 6, 7.9, “hi”, [25, “ships”]] Zero-based Slice list l1[start:end: step] #element at stop not included. All of them are optional
2 ways of creating a tuple
T1 = 2, 3, 4 # defines a tuple - immutable t2 = (5, 6, "days", -.5) # defines another tuple
Print Tuple
Print (t1) print(t2)
Change Tuples Elements
Can't change Elements in tuples
Key Tuples Information:
Similar to lists Values cannot be changed Declared using ( ) e.g. t1 = (“a”, “b”, “c”) or no delimiter e.g. t2 = 1, 2, 3 Convenient for returning multiple values from a function
Create Dictionaries
Commodities = {"corn":3.46 , "wheat": 4.40 , "soybeans" : 9.3} OR myComm = commodities.get("barley", 8.5) # assigns 8.50
Access key dictionaries
Print(commodities.keys("wheat")) #4.40
Access Value Dictionaries:
Print(commodities.values(9.3)) #Soybeans
Add Key:Value:
Commodities["quinoa"] = 10.3 # adds another key:value pair
Key Dictionaries Information:
Unordered set of pairs Associates values with keys Get/Set value by key (explicit or implicit) Check for key existence
Create set
Rainbow = {"red", "orange", "yellow", "green", 7, "blue", "indigo", [“pink”, “orange”],"violet"} # may have mixed types but not mutable types like lists, sets, dict theColors = rainbow
Set add
TheColors.add("brown") #add an item theColors.add(“yellow”) #no change, already there
Set.update():
TheColors.update(["black", "white", "purple“]) #add multiple
Key Sets Information:
Unordered Unindexed No duplicates Use loop structure to traverse Use ‘in’ to search
Create Numpy
Import numpy as np # Add the NumPy module with alias aName0 = np.array(25) #create a 0_d array aName = np.array([1,2,3,4,5,6,7,8]) # create a 1-D numpy array. Made up of 0-D arrays aName2 = np.array([[1,2,3,4],[5,6,7,8]]) # create a 2-D array. Made up of 1-D arrays
Print numpy
Print(aName[6]) print(aName2) print(aName2[1,3])
Np.arrange:
Np.arange(start, stop, step) Will not include the value stop. Default start is 0 Default step is 1
Key Numpy Information:
Faster than lists
Function Format
Def fname(parameters): # default values may be assigned to parameters “”” description “”” Statements return result
Number 10 function
Def cube_Num_or_10(theNumber = 10): return theNumber * theNumber * theNumber print(cube_Num_or_10(5), cube_Num_or_10())
If Statements(excute code conditionally):
If condition: ---- Statements # note the indentation to define the scope
Elif Statements:
If condition1: --------statements1 elif condition2: # more that one elif section allowed --------statements2
Else Statements:
If condition1: ------statements1 elif condition2: # 0 or more elif sections allowed -------statements2 else: # if all prior options are false --------statements3
Nested Statement
If condition: ----statements1 if condition2 #nested if ------statements2 else: ------statements3
Example of If Statement Used for Temperture:
Temp = 60 if temp > 85 : ------print ("temp is {}, scorching summer day" .format(temp)) elif temp > 65 : ------print ("temp is {}, comfortable summer day" .format(temp)) elif temp >45 : -------print ("temp is {}, summer must be leaving" .format(temp)) else : -------print ("temp is {}, feels like winter" .format(temp))
While Statement(excute code repeatly):
While condition: -------Statements #at least one statement must make condition false else: ------statements #execute when condition becomes false break # to exit loop while condition still true continue # to exit iteration
Example of While loop with num:
# WHILE construct num = 11 while num > 0: ----num -=1 ---- if num % 4 == 0: ------- #skip 0 and multiples of 4 ------- print("skip multiples of 4") continue -----print(num) -----print("looping's over")
For loop else statement:
For var in sequence: -----statements else: -----statements2 #execute once on exiting loop
For loop nest statement:
For var1 in sequence1: ---- for var2 in sequence2: #nested loop -----------statements
For loop variable:
For var in range(start, end, inc) #default start is 0, inc is 1 statements #execute -------Statements fixed number of times
Example of for loop with studennts:
# FOR loop students = ["john","jean", "juan" , "johan"] for person in students: ---print (person)
Example of for loop using range:
For evenNum in range(2,30, 2): ----print(evenNum)
Example of for loop using random number:
Import random teamAges = [] for x in range (15): -----teamAges.append (random.randint (10 ,25)) print (teamAges
List Compresion Key Information:
-Transform a list into another list -Choose elements - Transform elements -Result is a list
List Compresion Format:
NewStruct= transform list 1 i.e. newStruct = [result for value in collection filter]
Minor List Compression Example:
TeamAges = [12,13,14,15,16,17,18,19,20,21,22,23,24,25] minors = [age for age in teamAges if age <18] print(minors)
List Compression Random Numbers:
TeamAges = [random.randint(10,25) for _ in range(15)] print(teamAges)
Key information on creat and call user function:
May not return anything Argument same number as parameters May be assignment to variable May be arguments to others Parameter referenced by mutables,vale(inmutable)
Little Function Example calling it;
Def myLilFunction(message="Beautiful day, eh?"): ------print(message) myLilFunction() #call function using default myLilFunction("Gorgeous day") #print this message
Key information on function Arguments Args:
Must be same in number as parameters. Use * before parameter name for arbitrary number of arguments (*args) Arguments passed as a collection ( tuple). Use [] to copy to a list
Key information on keyword argument Args:
Argument's passed using keyword = value pairs Use ** before parameter name for arbitrary number of arguments (**kwargs) Arguments passed as a collection (dictionary)
Panda Key information
A module with tools for working with data. Remember NumPy, another module? • Must be added – import panda as pd • Think DataFrame -2D structure Data, column names, row indices
Create Data Frame:
My_df2 = pd.DataFrame({'Store':["NY1", "NY2",“NY3"], "Age":[10, 8, 5], "Employees":[3, 6, 5], "profit":[100, 189, 127]}) # optional columns= [list of column names], index = [list of indices] to order columns or specify indices
Passing a list of argument to Data Frame:
My_df3 =pd.DataFrame( [['NY1', 10, 3, 100], ['NY2', 8, 3, 189], ['NY3', 5, 5, 100]], index = ['a', 'b', 'c’],columns= ['Store', 'Age', 'Employees', ' Profit'])
Counting Subjects:
Bg_df['Gender'].count() bg_df.Gender.count() #Same as above
Average Age:
Bg_df['Age'].mean()
Male Statements:
Bg_df[bg_df.Gender=="M"].count()
Average Male statement:
Bg_df[bg_df.Gender=="M"].Age.mean()
Groupby Key information:
To group data and perform aggregate functions on the groups within the data -Note the non-numeric columns are omitted from sum(), mean(), … with numeric_only = True -Grouping may be done by columns (axis=1) e.g. using data types • Multilevel grouping (using multiple values is also possible e.g. by StoreState, StoreType
Groupby Aggregate:
Bg_df.groupby('Gender').Age.agg(['mean', 'max', 'min', 'median'])
Groupby Example:
Df.groupby(‘StoreState’) -groups them by state df.groupby(‘StoreState’).sum(), -groups the state and find the sum of all categories