Data Science 1 exam
🇬🇧
In Inglés
In Inglés
Practique preguntas conocidas
Manténgase al día con sus preguntas pendientes
Completa 5 preguntas para habilitar la práctica
Exámenes
Examen: pon a prueba tus habilidades
Pon a prueba tus habilidades en el modo de examen
Aprenda nuevas preguntas
Popular en este curso
Aprende con fichas
Modos dinámicos
InteligenteMezcla inteligente de todos los modos
PersonalizadoUtilice la configuración para ponderar los modos dinámicos
Modo manual [beta]
Seleccione sus propios tipos de preguntas y respuestas
Otros modos disponibles
Completa la oración
Escuchar y deletrearOrtografía: escribe lo que escuchas
elección múltipleModo de elección múltiple
Expresión oralResponde con voz
Expresión oral y comprensión auditivaPractica la pronunciación
EscrituraModo de solo escritura
Data Science 1 exam - Marcador
Data Science 1 exam - Detalles
Niveles:
Preguntas:
65 preguntas
| 🇬🇧 | 🇬🇧 |
| 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 |