How to add a column with values using the values of two other lists and another column?

Mystic 0
Viewed 49

Original df contains millions of rows. Here's an example:

df_sample = pd.DataFrame(
    {'keyword': {0: 756, 1: 756, 2: 13586, 3: 1694, 4: 13586}}
)
df_sample

enter image description here

Now we have two lists:

list_a = [756, 13586, 1694]
list_b = [1.44, 4.55, 10]

And I need the following output:

enter image description here

df_output = pd.DataFrame(
    {'keyword': {0: 756, 1: 756, 2: 13586, 3: 1694, 4: 13586},
     'Standard_Deviation_keyword': {0: 1.44, 1: 1.44, 2: 4.55, 3: 10, 4: 4.55}}
)
df_output

I guess solution would be something like:

def key_std(df):

    add a new column = Standard_Deviation_keyword

    for every x value of df.keyword:
      if x == "a value" in list_a:
        find the value at the same index in list_b
        and add that value to the same row in
        Standard_Deviation_keyword column
dataframe pandas numpy python
3 Answers

zip the lists and create a mapping dict then use Series.map to substitute values

df['std'] = df['keyword'].map(dict(zip(list_a, list_b)))

   keyword    std
0      756   1.44
1      756   1.44
2    13586   4.55
3     1694  10.00
4    13586   4.55
userawQR0

You can also use df.replace which will not give NaN unlike map if there is no corresponding elements in the lists:

df_sample['std'] = df_sample.keyword.replace(list_a, list_b)
Nuri Taş0

You can use map with a Series:

s = pd.Series(list_b, index=list_a)
df_sample['std'] = df_sample['keyword'].map(s)

output:

   keyword    std
0      756   1.44
1      756   1.44
2    13586   4.55
3     1694  10.00
4    13586   4.55
mozway0
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