Engineering numerical features

Preprocessing for Machine Learning in Python

James Chapman

Curriculum Manager, DataCamp

print(temps)

     city  day1  day2  day3
0     NYC  68.3  67.9  67.8
1      SF  75.1  75.5  74.9
2      LA  80.3  84.0  81.3
3  Boston  63.0  61.0  61.2

temps["mean"] = temps.loc[:,"day1":"day3"].mean(axis=1)
print(temps)

     city  day1  day2  day3   mean
0     NYC  68.3  67.9  67.8  68.00
1      SF  75.1  75.5  74.9  75.17
2      LA  80.3  84.0  81.3  81.87
3  Boston  63.0  61.0  61.2  61.73

Dates

print(purchases)

               date purchase
0      July 30 2011   $45.08
1  February 01 2011   $19.48
2   January 29 2011   $76.09
3     March 31 2012   $32.61
4  February 05 2011   $75.98

Dates

purchases["date_converted"] = pd.to_datetime(purchases["date"])

purchases['month'] = purchases["date_converted"].dt.month

print(purchases)

               date purchase date_converted  month
0      July 30 2011   $45.08     2011-07-30      7
1  February 01 2011   $19.48     2011-02-01      2
2   January 29 2011   $76.09     2011-01-29      1
3     March 31 2012   $32.61     2012-03-31      3
4  February 05 2011   $75.98     2011-02-05      2

Let's practice!

Preprocessing for Machine Learning in Python