遍历 dataframe 和字典以更新 dataframe 中的值以匹配字符串与 python

Question

I have a dictionary:

dict = {"name1":["name1_a, "name1_b"], "name2":["name2_a", "name2_b", "name2_c"]

Then I read in a.csv file as a dataframe that has the following structure:

df = pd.read_csv('file.csv')

Name	Value
"name1"	10
"name1_b"	30
"name2_c"	30

I need a function to iterate through the dataframe and the dictionary, in a way that it searches the dataframe for each name in the dictionary lists ("name1_a", "name1_b", etc). Once it finds a match, let's say for "name1_b", it should add the corresponding value (30) to "name1" in the dataframe. If the name doesn't exist in the dataframe (like "name2" in the example), it should create a new row and assign the value corresponding to the sum of "name2_a" + "name2_b", etc.

So the resulting dataframe should be like this (value of "name_1b" was added to the value of "name1", and "name2" was created and assigned the value of "name2_c):

Name	Value
"name1"	40
"name1_b"	30
"name2_c"	30
"name2"	30

Thanks for the help!

Answer 1

You could index df by name and create a separate dataframe that holds values that will be added to df. Some target keys in dict won't be in df , so they will need to be added with a default. Its similar with the addend lists in dict , some will not have values and will need a default.

Once those two are setup, you can loop through the addends, collect sums and add those to df .

import pandas as pd

df = pd.DataFrame({"Name":["name1", "name1_b", "name2_c"],
    "Value":[10, 30, 30]})

# map of target:addends to apply to dataframe
mydict = {"name1":["name1_a", "name1_b"], "name2":["name2_a", "name2_b", "name2_c"]}

# index dataframe by name and default unknown values
df.set_index("Name", inplace=True)
unknowns = pd.DataFrame(index=mydict.keys()-df.index)
unknowns["Value"] = 0
df = df.append(unknowns)
del unknowns

# create dataframe addends, defaulting unknown values
addends_df = pd.DataFrame(index={val for values in mydict.values() 
        for val in values})
addends_df["Value"] = df
addends_df.fillna(0, inplace=True)

# for each target, add the addends
for target, addends in mydict.items():
    df.loc[target] += addends_df.loc[addends].sum()

print(df)

Answer 2

Iterate through the dictionary items and mask the data frame from the matching key and value list and get the sum value using.sum(). if a specific name exists in the data frame simply assign the value else create a new row.

dict_ = {"name1":["name1_a", "name1_b"], "name2":["name2_a", "name2_b", "name2_c"]}

for k,v in dict_.items():
    mask_list = v + [k]
    sum_value = df[df['Name'].isin(mask_list)]['Value'].sum()

    if k in df['Name'].unique():
        df.loc[df['Name'] == k, 'Value'] = sum_value
    else:
        df.loc[len(df.index)] = [k, sum_value] 

Answer 3

You can try firstly via dict comprehension make a key:value pair out of the list then chack if 'Name' present in dd and filter out results then replace the values of 'Name' with their values by using replace() and assign() to assign the changes back then append this new dataframe in the original one and then groupby 'Name' and calculate sum:

d={"name1":["name1_a", "name1_b"], "name2":["name2_a", "name2_b", "name2_c"]}
dd={i:k for k,v in d.items() for i in v}
df=(df.append(df[df['Name'].isin(dd)]
      .assign(Name=lambda x:x['Name'].replace(dd)))
      .groupby('Name',as_index=False).sum())

OR

The same approach but in seperate steps:

d={"name1":["name1_a", "name1_b"], "name2":["name2_a", "name2_b", "name2_c"]}
dd={i:k for k,v in d.items() for i in v}
df1=df[df['Name'].isin(dd)]
df1['Name']=df1['Name'].map(dd)
df=df.append(df1,ignore_index=True)
df=df.groupby('Name',as_index=False)['name2'].sum()

output of df :

    Name        name2
0   name1       40
1   name1_b     30
2   name2       30
3   name2_c     30

Note: don't assign anything to dict function in python