遍历 pandas 列中的字典列表并创建新列

Question

I'd like to parse json dictionaries from a pandas dataframe column, iterate over the dicts and assign them to new column values.

Here's a column of dataframe: df['Column'][0]

[{'Name': 'Vacant', 'Value': 3904000, 'Unit': 'Qty'},
 {'Name': 'Vacant', 'Value': 11.7, 'Unit': 'Pct'},
 {'Name': 'Absorption', 'Value': 415000, 'Unit': 'Units'},
 {'Name': 'AbsorpOcc', 'Value': 1.4, 'Unit': 'Pct'},
 {'Name': 'Occupied', 'Value': None, 'Unit': 'Qty'}]

I have the following code to iterate over each row in pandas dataframe, and then iterate over each dicts in a list and create new columns.

# Iterate over dataframe to parse select rows   
# Declare array
s = ""

#Iterate over each row in Dataframe
for index, row in df.iterrows():
    
    # Iterate over each json object in each row in DataFrame
    for i in range(0,len(row['Column'])):
        
        for k,v in row['Column'][i].items():
            
            # Concat string labels to assign them as column names
            if type(v) == str:
            
                s += v
            
        print(s)
                  

Expected Output, new columns:

Answer 1

You have a specific requirement to process the 'Column' column of the dataframe. I think you should use apply https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html . Also this change would be in place in dataframe so your function could be.

def func(row):
    # your parsing logic
    index = row.name
    # {'Name': 'Vacant', 'Value': 3904000, 'Unit': 'Qty'}
    # col = 'Vacant', value = 3904000
    df.loc[index, col] = value
df.apply(func, axis=1)