Explore the students ' consumption of wine
Data See GitHub
Step 1-Import the necessary libraries
Import Pandas as PD Import NumPy as NP
Step 2-Data set
" ./data/student-mat.csv "
Step 3 Name The data student
Student = Pd.read_csv (PATH4) Student.head ()
Output:
Step 4 Slice the data from ' school ' to ' Guardian '
" School ":"Guardian"]stud_alcoh.head ()
Output:
Step 5 Create a lambda function that captures a string
Lambda x:x.upper ()
Step 6 capitalize the ' Fjob ' column
stud_alcoh['Fjob'].apply (Captalizer)
Output:
0 TEACHER1 OTHER2 OTHER3 SERVICES4 OTHER5 OT HER6 OTHER7 TEACHER8 OTHER9 OTHER10 HEALTH11 OTHER12 SERVICES13 othe R14 OTHER15 OTHER16 SERVICES17 OTHER18 SERVICES19 OTHER20 OTHER21 HEALTH2 2 OTHER23 OTHER24 HEALTH25 SERVICES26 OTHER27 SERVICES28 OTHER29 TEACHER ... 365 OTHER366 SERVICES367 SERVICES368 SERVICES369 TEACHER370 SERVICES371 SERVICES372 at_home37 3 OTHER374 OTHER375 OTHER376 OTHER377 SERVICES378 OTHER379 OTHER380 TEACHER381 OTHER382 SERVICES383 SERVICES384 OTHER385 OTHER386 at_home387 OTHER388 SERVICES389 OTHER390 SERVICES391 SERVICES392 OTHER393 OTHER394 at_homename:fjob, Dtype:object
Step 7 Print the last few line elements of the dataset
Stud_alcoh.tail ()
Output:
Step 8 Notice that the original data frame is still lowercase and then improve
stud_alcoh['mjob'= stud_alcoh['mjob'].apply ( Captalizer) stud_alcoh['Fjob'] = stud_alcoh['Fjob ' ].apply (Captalizer) stud_alcoh.tail ()
Output:
Step 9 Create a function named majority, which returns a Boolean value to a new column named Legal_drinker (most older than 17 years old)
def majority (x): if x >: return TrueElse: return False
stud_alcoh[' legal_drinker'= stud_alcoh['age'].apply ( Majority) Stud_alcoh.head ()
Output:
Step 10 multiply each number of datasets by 10
def TIMES10 (x): if is int: return * x return x
Stud_alcoh.applymap (TIMES10). Head (10)
Output:
Pandas Exercise (iv)---apply apply function