Pandas df 根据与不同列中的行匹配的字典中的值更改一列中的行的值

Question

I have a df pictured below and I want to change the value of the "sic_code" depending on the "code"

I have created a dictionary:

comp_dict = dict(zip(sic_dict_keys, sic_dict_values))

and was thinking of something like this but then got stuck. I basically want to change the value of sic_code if the code number is in my dictionary eg changing the sic_code 2834 to 3000 for the code 1611787

for key in comp_dict:
    if df.loc[df["code"] == key]:

Answer 1

Pandas DataFrame s have a replace method for exactly this operation:

import pandas as pd

df = pd.DataFrame(data={'a': [1, 2, 3], 'b': [100, 200, 300]})
rename_dict = {100: 1000, 200: 2000}

df['b'].replace(rename_dict, inplace=True)

print(df)

which results in:

   a     b
0  1  1000
1  2  2000
2  3   300

You can leave out the inplace=True if you prefer to return a copy.

Answer 2

I'm not sure what you want to do, I wrote the code so that if a row has a sic_code corresponding to a key of comp_dict, the sic_code is changed to the corresponding value of comp_dict. Tell me if I got you wrong. For the loop, I'd rather iterate the dataframe rather than the dictionnary. For instance, by using iterrows and iloc , a simple loop could look like this:

for index, row in df.iterrows():
   sic_code = row['sic_code']
   if sic_code in comp_dict.keys():
      df.iloc[index, <index of column sic_code>] = comp_dict[sic_code] 

Here, if your dict contains {2834: 3000}, all the rows with a sic_code value 2834 will be changed to 30000.

Answer 3

Here is a two-step approach to the question. First, find the data frame entries that exist in the code-to-sic code dictionary. Second, use the .map() function to update the sic code:

df = (pd.DataFrame(
    {'code': [1611787, 170846, 142529],
     'name': ['Advanced', 'Perth', 'ATA Creativity'],
     'sic_code': [2834, 6221, 8200]})
      .set_index('code')
     )

# key is `code`; value is `sic_code`
comp_dict = {1611787: 3000}

# find data frame entries such that `code` is in the dictionary
mask = df.index.isin(comp_dict)

# update `sic_code`
df.loc[mask, 'sic_code'] = df.index[mask].map(comp_dict)
df

The resulting data frame is:

                   name  sic_code
code                             
1611787        Advanced      3000
170846            Perth      6221
142529   ATA Creativity      8200

Answer 4

I got this to work how I want it to but I don't know if its the most efficient.

for index, row in df.iterrows():
        print(row['code'], row['sic_code'])
        for key in comp_dict:
            # print(key)
            if row['code'] == key:
                df['sic_code'][index] = comp_dict[key]