如何根据条件将值添加到新列？

Question

I am trying to put a new column to my dataset according to condition, however, resulting dataframe is not what I am expecting.

I've tried some approaches and this is closest what I've been.

import pandas as pd

data = {'Date' : ['3-Mar', '20-Mar', '20-Apr', '21-Apr', '29-Apr', '7- 
         May', '30-May', '31-May', '7-Jun', '16-Jun',
        '1-Jul', '2-Jul', '10-Jul'],
        'Value' : [0.5840, 0.8159, 0.7789, 0.7665, 0.8510, 0.7428, 0.7124, 
        0.6820, 0.8714, 0.8902, 0.8596, 0.8289, 0.6877],}
frame = pd.DataFrame(data)

for counter, value in enumerate(frame['Value']):
    if value >= 0.7:
        frame = frame.append({'result': 'High'}, ignore_index=True)   
    else:
        frame = frame.append({'result': 'Low'}, ignore_index=True)   

print(frame)

Result is:

     Date   Value result
0    3-Mar  0.5840    NaN
1   20-Mar  0.8159    NaN
2   20-Apr  0.7789    NaN
3   21-Apr  0.7665    NaN
4   29-Apr  0.8510    NaN
5    7-May  0.7428    NaN
6   30-May  0.7124    NaN
7   31-May  0.6820    NaN
8    7-Jun  0.8714    NaN
9   16-Jun  0.8902    NaN
10   1-Jul  0.8596    NaN
11   2-Jul  0.8289    NaN
12  10-Jul  0.6877    NaN
13     NaN     NaN    Low
14     NaN     NaN   High
15     NaN     NaN   High
16     NaN     NaN   High
17     NaN     NaN   High
18     NaN     NaN   High
19     NaN     NaN   High
20     NaN     NaN    Low
21     NaN     NaN   High
22     NaN     NaN   High
23     NaN     NaN   High
24     NaN     NaN   High
25     NaN     NaN    Low

However, I am expecting that values will be placed next to the existing ones not new ones.

Thank you!

Answer 1

If you look at the documentation of the append function, you'll see that it appends rows to the end of the dataframe which is not what you want:

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.append.html

You can achieve this with a lambda function, which essentially iterates over every row applying whatever logic you want.

frame['result'] = frame['Value'].apply(lambda x: 'High' if x > .7 else "Low")

Answer 2

If I understand well, this is probably already answered but here you go

you need to create a new column result

define a function (for readability) which takes a value and returns the result

def udf(value):
    if value >= .7:
        return "High"
    else
        return "Low"

then apply this function to the column value

frame['result'] = frame['Value'].apply(udf)

I suggest you read the doc the DataFrame.apply

Answer 3

Using pandas.Series could fix your issue

import pandas as pd

data = {'Date' : ['3-Mar', '20-Mar', '20-Apr', '21-Apr', '29-Apr', '7- May', 
                  '30-May', '31-May', '7-Jun', '16-Jun','1-Jul', '2-Jul', '10-Jul'],
        'Value' : [0.5840, 0.8159, 0.7789, 0.7665, 0.8510, 0.7428, 0.7124, 
                   0.6820, 0.8714, 0.8902, 0.8596, 0.8289, 0.6877]}
frame = pd.DataFrame(data)
frame['result'] = pd.Series(['High' if x >= 0.7 else 'Low' for x in frame['Value']])

Output :

Date    Value   result
0   3-Mar   0.5840  Low
1   20-Mar  0.8159  High
2   20-Apr  0.7789  High
3   21-Apr  0.7665  High
4   29-Apr  0.8510  High
5   7- May  0.7428  High
6   30-May  0.7124  High
7   31-May  0.6820  Low
8   7-Jun   0.8714  High
9   16-Jun  0.8902  High
10  1-Jul   0.8596  High
11  2-Jul   0.8289  High
12  10-Jul  0.6877  Low