Finding values in a column that are same and making separate dataframes of them

Question

I have a pandas dataframe that contains two columns, one columns has many values and the other column only contains values most of which are same. This is what the dataframe looks like:

Item        Price

Apple        10
Banana        5
Mango        10 
Pineapple     7 
Kiwi          5
Tomatoes      2 
Eggs         10
Potatoes      7
Burgers       5
Milk          2
Chicken      10
Coffee        7
Noodles       5

The values on the price column change. I want to be be able to filter the items for which the prices are same and create a new dataframe from them. What I'm not being able to do is the look for different prices which occur, if the prices were to stay the same then I am able to filter the items based on that but I can't when the prices arbitrarily change. This is what I want to achieve, it is only a dataframe for one particular price as an example.

Item       Price

Apple        10
Mango        10
Eggs         10
Chicken      10

You can convert groupby object to tuples and then to dict and access each group as key of dictionary. If you need on further info on how to do it check out the link attached to this post. — Sumanth, Jan 28 '19 at 11:27

Johny Jose · Answer 1 · 2019-01-28T11:26:39.187

1

You can use dataframe function for getting the subset.

price_list = df.Price.unique()
sets = []
for price in price_list:
    sets.append(df[df.Price == price])

edited Jan 28 '19 at 11:26

answered Jan 28 '19 at 11:19

Johny Jose

11
4

I know this already, the problem is that I don't know the prices of the newer dataframes in which they change arbitrary. I can only use this if I know which are the prices that exist in the dataframe which is quite large and to pin point each price is humanly impossible – Faisal Afzal Jan 28 '19 at 11:21
Sorry for the confusion, you could get unique values of price at any given point `df.Price.unique()` and then use that list to obtain the seperate new dataframes – Johny Jose Jan 28 '19 at 11:23

score 1 · Accepted Answer · answered Jan 28 '19 at 11:24

1

You can do this using below:

df_new = []
for i in df['Price'].unique():
    df1 = df[df['Price']==i]
    df_new.append(df1)

print(df_new[0])

Output:

Item       Price

Apple        10
Mango        10
Eggs         10
Chicken      10

answered Jan 28 '19 at 11:24

Sociopath

13,068
19
47
75

But this create a list of all the dataframes combined while I need each dataframe to be separate. – Faisal Afzal Jan 28 '19 at 12:11
Use `index` to access each dataframe separatly. `df1=df_new[0]` and so on... – Sociopath Jan 28 '19 at 12:37

Finding values in a column that are same and making separate dataframes of them

2 Answers2