How to perform operation over each dictionary in column?

user_17378883 0
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If I have a column in dataframe with dictionaries:

col1
{10:24, 7:3}
{5:24, 1:2, 7:8}
{1:1}

How to perform operation of extracting keys from each dictionary for each rows? So I need to get:

col1
10, 7
5, 1, 7
1

How to do that? df["col1"] = df["col1"].keys() doesnt work and I don't know why

dataframe python-3.x dictionary python
3 Answers

DataFrame has .keys() to get own indexes, not to get keys from dictionares in cells.

But you can use .apply() to run function on every elemement in column separatelly.

df['col1'] = df['col1'].apply(lambda item: item.keys())

Minimal working example:

import pandas as pd

df = pd.DataFrame({'col1':[
   {10:24, 7:3},
   {5:24, 1:2, 7:8},
   {1:1},
]})


df['col1'] = df['col1'].apply(lambda item: item.keys())

print(df)

Result (now it has tuples with numbers):

        col1
0    (10, 7)
1  (5, 1, 7)
2        (1)

BTW:

DataFrame has special method to work with strings .str which may work also with list/tuples and some of them even with dictionary

It can't get df['col1'].str.keys() because string doesn't have keys but if you use df['col1'].str[10] then you get from all dictionares elements which have key 10

0    24.0
1     NaN
2     NaN
user_18320580

df["col1"] is not a dictionary - it is a tuple. That explains why you get an AttributeError. You need to iterate over each row in the dataframe column and call keys() on it.

df['col1'] = [row.keys() for row in df["col1"]]
user_126640400

DataFrame.apply according to the documentation:

Apply a function along an axis of the DataFrame.

Luckily, the default axis is columns, not rows. You are going for a single column, so make your applied function check the current column.

df.apply(lambda c: c.keys() if c.name == "col1" else c)
user_40097870
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