How to change values in a Python dataframe
I've been looking for an answer for the last 30 minutes, but the only solutions are either for a single column or for R. I have a dataset where I want to change the values ('Y / N') to 1 and 0 respectively. I feel like copying and pasting the code below 17 times is very inefficient.
df.loc[df.infants == 'n', 'infants'] = 0
df.loc[df.infants == 'y', 'infants'] = 1
df.loc[df.infants == '?', 'infants'] = 1
My solution is as follows. This does not raise an error, but the values in the data frame do not change. I guess I need to do something like df = df_new. But how to do that?
for coln in df:
for value in coln:
if value == 'y':
value = '1'
elif value == 'n':
value = '0'
else:
value = '1'
EDIT: There are 17 columns in this dataset, but there is another dataset that I hope to solve which contains 56 columns.
republican n y n.1 y.1 y.2 y.3 n.2 n.3 n.4 y.4 ? y.5 y.6 y.7 n.5 y.8
0 republican n y n y y y n n n n n y y y n ?
1 democrat ? y y ? y y n n n n y n y y n n
2 democrat n y y n ? y n n n n y n y n n y
3 democrat y y y n y y n n n n y ? y y y y
4 democrat n y y n y y n n n n n n y y y y
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I think it is easiest to use on : replace
dict
np.random.seed(100)
df = pd.DataFrame(np.random.choice(['n','y','?'], size=(5,5)),
columns=list('ABCDE'))
print (df)
A B C D E
0 n n n ? ?
1 n ? y ? ?
2 ? ? y n n
3 n n ? n y
4 y ? ? n n
d = {'n':0,'y':1,'?':1}
df = df.replace(d)
print (df)
A B C D E
0 0 0 0 1 1
1 0 1 1 1 1
2 1 1 1 0 0
3 0 0 1 0 1
4 1 1 1 0 0
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Maybe you can try to apply,
import pandas as pd
# create dataframe
number = [1,2,3,4,5]
sex = ['male','female','female','female','male']
df_new = pd.DataFrame()
df_new['number'] = number
df_new['sex'] = sex
df_new.head()
# create def for category to number 0/1
def tran_cat_to_num(df):
if df['sex'] == 'male':
return 1
elif df['sex'] == 'female':
return 0
# create sex_new
df_new['sex_new']=df_new.apply(tran_cat_to_num,axis=1)
df_new
raw
number sex
0 1 male
1 2 female
2 3 female
3 4 female
4 5 male
after use apply
number sex sex_new
0 1 male 1
1 2 female 0
2 3 female 0
3 4 female 0
4 5 male 1
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