Python Pandas - How can I make .replace function work with dict and conditionals?-CodePudding

I'm trying to change a .CSV row data. So for the new data I have a dict. I want to say python that if the keys in my dict are equal to the items row in my .csv then change the data in another row.

The CSV file looks something like:

ID | Value_1 | Value_2  | Value_3 | 
1  | info    | changeme | info    |
1  | info    | changeme | info    |
2  | info    | changeme2| info    |
3  | info    | changeme3| info    |

and the dict looks something like:

dictionary = {1: 'info1', 2: 'info2', 3: 'info3'}

Note that in the .csv file the ID's could be repeated, but not in the dict (because it works like that). So when I acces to the key in my dictionary and that key is equal to the ID in the .csv file, the value_2 row (as an example) have to change it's content for the value of the key into the dictionary.

I hope y'all understand my explanation :/

Then I have tried something like this. But I don't really know if the problem is with my coding or with pandas:

for key, values in dictionary.items():
    if list(str(keys)) == list(df['ID']):
        df['VALUE'].replace(to_replace='VALUES', value= values, inplace= True)

but it's not working. Also tried that outside the for loop, and without the if. It just don't work. But creates a new row indicating like the len of the .csv file.

Maybe I don't have to use pandas for doing this? Any advise would be helpful!

CodePudding user response：

I am pretty sure this is what you want - though not very clear from your question.

# assuming df is this
"""
ID | Value_1 | Value_2  | Value_3 | 
1  | info    | changeme | info    |
1  | info    | changeme | info    |
2  | info    | changeme2| info    |
3  | info    | changeme3| info    |
"""
dictionary = {1: 'info1', 2: 'info2', 3: 'info3'}

df["ID"] = df["ID"].astype(int)
df["column_with_mapped_value"] = df["ID"].replace(to_replace=dictionary)
# output
"""
   ID Value_1    Value_2 Value_3 column_with_mapped_value
0   1    info   changeme    info   info1
1   1    info   changeme    info   info1
2   2    info  changeme2    info   info2
3   3    info  changeme3    info   info3
"""

CodePudding user response：

To iterate through a dataset checking a condition I usually use the apply method. In your case, would be like this:

import pandas as pd

df = pd.read_csv('teste.csv', sep=';')

# ID Value_1   Value_2 Value_3
# 1    info   changeme    info
# 1    info   changeme    info
# 2    info   changeme    info
# 3    info   changeme    info
# 5    info   changeme    info

dictionary = {1: 'info1', 2: 'info2', 3: 'info3', 4: 'info4'}

df['Value_2'] = df.apply(lambda x: dictionary[x.ID] if x.ID in dictionary else x.Value_2, axis=1)

# ID Value_1   Value_2 Value_3
# 1    info     info1    info
# 1    info     info1    info
# 2    info     info2    info
# 3    info     info3    info
# 5    info  changeme    info

I added one more line in the csv and in the dict to test what happens if a key don't exist in one of them.