Dictionary column in pandas dataframe

Question

I\'ve got a csv that I\'m reading into a pandas dataframe. However one of the columns is in the form of a dictionary. Here is an example:

ColA, ColB, ColC, C         


        

Answer 1

What about something like:

import pandas as pd

# Create mock dataframe
df = pd.DataFrame([
    [20, 30, {'ab':1, 'we':2, 'as':3}, 'String1'],
    [21, 31, {'ab':4, 'we':5, 'as':6}, 'String2'],
    [22, 32, {'ab':7, 'we':8, 'as':9}, 'String2'],
], columns=['Col A', 'Col B', 'Col C', 'Col D'])

# Create dataframe where you'll store the dictionary values
ddf = pd.DataFrame(columns=['AB','WE','AS'])

# Populate ddf dataframe
for (i,r) in df.iterrows():
    e = r['Col C']
    ddf.loc[i] = [e['ab'], e['we'], e['as']]

# Replace df with the output of concat(df, ddf)
df = pd.concat([df, ddf], axis=1)

# New column order, also drops old Col C column
df = df[['Col A', 'Col B', 'AB', 'WE', 'AS', 'Col D']]

print(df)

Output:

   Col A  Col B  AB  WE  AS    Col D
0     20     30   1   2   3  String1
1     21     31   4   5   6  String2
2     22     32   7   8   9  String2

Answer 2

So starting with your one row df

    Col A   Col B   Col C                           Col D
0   20      30      {u'we': 2, u'ab': 1, u'as': 3}  String1

EDIT: based on the comment by the OP, I'm assuming we need to convert the string first

import ast
df["ColC"] =  df["ColC"].map(lambda d : ast.literal_eval(d))

then we convert Col C to a dict, transpose it and then join it to the original df

dfNew = df.join(pd.DataFrame(df["Col C"].to_dict()).T)
dfNew

which gives you this

    Col A   Col B   Col C                           Col D   ab  as  we
0   20      30      {u'we': 2, u'ab': 1, u'as': 3}  String1 1   3   2

Then we just select the columns we want in dfNew

dfNew[["Col A", "Col B", "ab", "we", "as", "Col D"]]

    Col A   Col B   ab  we  as  Col D
0   20      30      1   2   3   String1

Answer 3

As per https://stackoverflow.com/a/38231651/454773, you can use .apply(pd.Series) to map the dict containing column onto new columns and then concatenate these new columns back into the original dataframe minus the original dict containing column:

dw=pd.DataFrame( [[20, 30, {"ab":"1", "we":"2", "as":"3"},"String"]],
                columns=['ColA', 'ColB', 'ColC', 'ColdD'])
pd.concat([dw.drop(['ColC'], axis=1), dw['ColC'].apply(pd.Series)], axis=1)

Returns:

ColA    ColB    ColdD   ab  as  we
20      30      String  1   3   2