How to convert Dictionary to Pandas Dataframe?

How about we examine how to change Python Dictionary over to Pandas Dataframe. We can change a word reference over to a pandas dataframe by utilizing the pd.DataFrame.from_dict() class-technique.

Use dict.items() to get a set-like article with the keys and upsides of dict. Utilize list(iterable) with iterable as the set-like item to change it over to a rundown. To make a DataFrame, use pandas.DataFrame(data) with this rundown as information.

data_dict = {"a": 1, "b": 2, "c": 3}
data_items = data_dict.items()
data_list = list(data_items)

df = pd.DataFrame(data_list)

create DataFrame from `data_list`

   0  1
0  a  1
1  b  2
2  c  3

The keys are Unicode dates and the qualities are numbers. I might want to change over this into a pandas dataframe by having the dates and their comparing esteems as two separate segments. Model: col1: Dates col2: DateValue (the dates are still Unicode and datevalues are still whole numbers)

Any assistance toward this path would be greatly valued. I can’t track down assets on the pandas docs to assist me with this.

I realize one arrangement may be to change over each key-esteem pair in this dict, into a dict so the whole construction turns into a dict of dicts, and afterward we can add each column independently to the dataframe. However, I need to know whether there is a more straightforward way and a more straightforward method for doing this.

Steps to Convert a Dictionary to Pandas DataFrame

Step 1: Gather the Data for the Dictionary

To start, gather the data for your dictionary.

For example, let’s gather the following data about products and prices:

Product Price
Computer 1500
Monitor 300
Printer 150
Desk 250

Step 2: Create the Dictionary

Next, create the dictionary.

For our example, you may use the following code to create the dictionary:

my_dict = {'Computer':1500,'Monitor':300,'Printer':150,'Desk':250}

print (my_dict)
print (type(my_dict))

Run the code in Python, and you’ll get the following dictionary:

{'Computer': 1500, 'Monitor': 300, 'Printer': 150, 'Desk': 250}
<class 'dict'>

Notice that the syntax of print (type(my_dict)) was add at the bottom of the code to confirm that we indeed got a dictionary.

Step 3: Convert the Dictionary to a DataFrame

For the final step, convert the dictionary to a DataFrame using this template:

import pandas as pd

my_dict = {key:value,key:value,key:value,...}
df = pd.DataFrame(list(my_dict.items()),columns = ['column1','column2']) 

For our example, here is the complete Python code to convert the dictionary to a DataFrame:

import pandas as pd

my_dict = {'Computer':1500,'Monitor':300,'Printer':150,'Desk':250}
df = pd.DataFrame(list(my_dict.items()),columns = ['Products','Prices'])

print (df)
print (type(df))

As you can see, the dictionary got converted to Pandas DataFrame:

   Products  Prices
0  Computer    1500
1   Monitor     300
2   Printer     150
3      Desk     250
<class 'pandas.core.frame.DataFrame'>

Note that the syntax of print (type(df)) was added at the bottom of the code to confirm that we actually got a DataFrame.

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