Python/how to insert data from one table into another according to the data condition - python-3.x

Question

I have a the first table
user_cnt cost acquisition_cost
channel
facebook_ads 2726 2140.904643 0.79
instagram_new_adverts 3347 2161.441691 0.65
yandex_direct 4817 2233.111449 0.46
youtube_channel_reklama 2686 1068.119204 0.40
and the second
user_id profit source cost_per_user income
8a28b283ac30 0.91 facebook_ads ? 0.12
d7cf130a0105 0.63 youtube_channel ? 0.17
The second table has more 200k rows, but i showed only two. So, i need to put "acquisition_cost" value from the first table to column "cost_per_user" in the second table according to the name of channel/source. For instance, on the first row in the second table cost_per_user should has value - 0.79 due to it's facebook_ads.
I will be grateful if someone can help me to solve this task.
First of all i tried to use the function:
I tried the function:
def cost (source_type):
if source_type == 'instagram_new_adverts':
return 0.65
elif source_type == 'facebook_ads':
return 0.79
elif source_type == 'youtube_channel_reklama':
return 0.40
else: return 0.46
target_build['cost_per_user'] = target_build['source'].apply(cost)`
but i have to find another desicion without using of constants(return 0.65).
Another attemption was like this
for row in first_table['channel'].unique():
second_table.loc[second_table['source'] == row, 'cost_per_user'] = first_table['acquisition_cost']
this code works only for the first four lines and for another it put zero value.
and the last idea was
second_table['cost_per_user'] = second_table['cost_per_user'].where(
second_table['source'].isin(b.index), b['acquisition_cost'])
and again it didn't work.

Answer 1

It looks like you are using pandas. One possible solution is to use pandas version of inner join: merge.
Example
Suppose you don't want to modify either your first or second table, you can create a temporary table including just channel and acquisition_cost from the first table, but also changing the column names to source and cost_per_user. This can be implemented in various ways. One possible way, presuming your channel is an index, is shown below.
temp_df = first_df.acquisition_cost.reset_index().rename(
columns={'channel': 'source', 'acquisition_cost': 'cost_per_user'},
)
temp_df looks like this
source cost_per_user
0 facebook_ads 0.79
1 instaram_new_adverts 0.65
2 yandex_direct 0.46
3 youtube_channel_reklama 0.40
Say your second table looks like this:
user_id profit source income
0 c3519e80c071 0.773956 yandex_direct 0.227239
1 cc39ba469a08 0.438878 instaram_new_adverts 0.554585
2 a44a621e0222 0.858598 facebook_ads 0.063817
3 9dbf921b0959 0.697368 youtube_channel_reklama 0.827631
4 d45bf8fcab75 0.094177 youtube_channel_reklama 0.631664
5 57dbe1efd8b1 0.975622 yandex_direct 0.758088
6 1e0e3f1e13f7 0.761140 instaram_new_adverts 0.354526
7 27a7a7470ef4 0.786064 youtube_channel_reklama 0.970698
8 360dfd543fb5 0.128114 yandex_direct 0.893121
9 a31f46c26abb 0.450386 instaram_new_adverts 0.778383
You can run the merge call to attach cost_per_user to each source.
new_df = pd.merge(left=second_df, right=temp_df, on='source', how='inner')
new_df would look like this
user_id profit source income cost_per_user
0 c3519e80c071 0.773956 yandex_direct 0.227239 0.46
1 57dbe1efd8b1 0.975622 yandex_direct 0.758088 0.46
2 360dfd543fb5 0.128114 yandex_direct 0.893121 0.46
3 cc39ba469a08 0.438878 instaram_new_adverts 0.554585 0.65
4 1e0e3f1e13f7 0.761140 instaram_new_adverts 0.354526 0.65
5 a31f46c26abb 0.450386 instaram_new_adverts 0.778383 0.65
6 a44a621e0222 0.858598 facebook_ads 0.063817 0.79
7 9dbf921b0959 0.697368 youtube_channel_reklama 0.827631 0.40
8 d45bf8fcab75 0.094177 youtube_channel_reklama 0.631664 0.40
9 27a7a7470ef4 0.786064 youtube_channel_reklama 0.970698 0.40
Notes
Complication would arise if the source column of the second table does not have a one-to-one match to the channel in the first table. You will need to read the doc on merge to decide how you would want to handle that situation (e.g. use inner join to discard any mismatch, or left join to keep the unmatched source but receiving no cost_per_user).