Data Science 1 exam
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Data Science 1 exam - Leaderboard
Data Science 1 exam - Details
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65 questions
| 🇬🇧 | 🇬🇧 |
| 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 |
| Numpy Symbol | .np |
| 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 Example: | Here |
| 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 |