如何使用python从csv文件中检索一列？

Question

im trying to retrieve the age column from one of the csv file , here is what i coded so far.

df = pd.DataFrame.from_csv('train.csv')
result = df[(df.Sex=='female') & (df.Pclass==3)]

print(result.Age)
# finding the average age of all people who survived


print len(result)
sum = len(result)

I printed out the age, because i wanted to see the list of all ages that belong to the colunm of sex that has the value of "female" and the column of class which has the value of "3"

the print result for some reason shows the colunm number and the age next to it, i just want it print the list of ages thats all.

PassengerId
3      26.0
9      27.0
11      4.0
15     14.0
19     31.0
20      NaN
23     15.0
25      8.0
26     38.0
29      NaN
33      NaN
39     18.0
40     14.0
41     40.0
45     19.0
48      NaN
50     18.0
69     17.0
72     16.0
80     30.0
83      NaN
86     33.0
101    28.0
107    21.0
110     NaN
112    14.5
114    20.0
115    17.0
120     2.0
129     NaN
       ... 
658    32.0
678    18.0
679    43.0
681     NaN
692     4.0
698     NaN
703    18.0
728     NaN
730    25.0
737    48.0
768    30.5
778     5.0
781    13.0
787    18.0
793     NaN
798    31.0
800    30.0
808    18.0
814     6.0
817    23.0
824    27.0
831    15.0
853     9.0
856    18.0
859    24.0
864     NaN
876    15.0
883    22.0
886    39.0
889     NaN
Name: Age, dtype: float64

This is what my program prints, i just want the list of age on the right column only not the passengerID column which is on the left.

Thank you

Answer 1

result.Age is a pandas Series object, and so when you print it, column headers, indices, and data types are shown as well. This is a good thing, because it makes the printed representation of the object much more useful.

If you want to control exactly how the data is displayed, you will need to do some string formatting. Something like this should do what you're asking for:

print('\n'.join(str(x) for x in result.Age))

---

If you want access to the raw data underlying that column for some reason (usually you can work with the Series just as well), without indices or headers, you can get a numpy array with

result.Age.values