How to create a dictionary of key : column_name and value : unique values in column in python from a dataframe

Question

I am trying to create a dictionary of key:value pairs where key is the column name of a dataframe and value will be a list containing all the unique values in that column.Ultimately I want to be able to filter out the key_value pairs from the dict based on conditions. This is what I have been able to do so far:

for col in col_list[1:]:
    _list = []
    _list.append(footwear_data[col].unique())
    list_name = ''.join([str(col),'_list'])

product_list = ['shoe','footwear']
color_list = []
size_list = []

Here product,color,size are all column names and the dict keys should be named accordingly like color_list etc. Ultimately I will need to access each key:value_list in the dictionary. Expected output:

KEY              VALUE
color_list :    ["red","blue","black"]
size_list:  ["9","XL","32","10 inches"]

Can someone please help me regarding this?A snapshot of the data is attached.

Solution 1

With a DataFrame like this:

import pandas as pd
df = pd.DataFrame([["Women", "Slip on", 7, "Black", "Clarks"], ["Women", "Slip on", 8, "Brown", "Clarcks"], ["Women", "Slip on", 7, "Blue", "Clarks"]], columns= ["Category", "Sub Category", "Size", "Color", "Brand"])

print(df)

Output:

  Category Sub Category  Size  Color    Brand
0    Women      Slip on     7  Black   Clarks
1    Women      Slip on     8  Brown  Clarcks
2    Women      Slip on     7   Blue   Clarks

You can convert your DataFrame into dict and create your new dict when mapping the the columns of the DataFrame, like this example:

new_dict = {"color_list": list(df["Color"]), "size_list": list(df["Size"])}
# OR:
#new_dict = {"color_list": [k for k in df["Color"]], "size_list": [k for k in df["Size"]]}

print(new_dict)

Output:

{'color_list': ['Black', 'Brown', 'Blue'], 'size_list': [7, 8, 7]}

In order to have a unique values, you can use set like this example:

new_dict = {"color_list": list(set(df["Color"])), "size_list": list(set(df["Size"]))}
print(new_dict)

Output:

{'color_list': ['Brown', 'Blue', 'Black'], 'size_list': [8, 7]}

Or, like what @Ami Tavory said in his answer, in order to have the whole unique keys and values from your DataFrame, you can simply do this:

new_dict = {k:list(df[k].unique()) for k in df.columns}
print(new_dict)

Output:

{'Brand': ['Clarks', 'Clarcks'],
 'Category': ['Women'],
 'Color': ['Black', 'Brown', 'Blue'],
 'Size': [7, 8],
 'Sub Category': ['Slip on']}

Solution 2

I am trying to create a dictionary of key:value pairs where key is the column name of a dataframe and value will be a list containing all the unique values in that column.

You could use a simple dictionary comprehension for that.

Say you start with

import pandas as pd

df = pd.DataFrame({'a': [1, 2, 1], 'b': [1, 4, 5]})

Then the following comprehension solves it:

>>> {c: list(df[c].unique()) for c in df.columns}
{'a': [1, 2], 'b': [1, 4, 5]}