How to convert list of dictionary to DataFrame in python

How do I convert a list of dictionaries to a pandas DataFrame?

The other answers are correct, but not much has been explained in terms of advantages and limitations of these methods. The aim of this post will be to show examples of these methods under different situations, discuss when to use [and when not to use], and suggest alternatives.

Depending on the structure and format of your data, there are situations where either all three methods work, or some work better than others, or some don't work at all.

Consider a very contrived example.

np.random.seed[0] data = pd.DataFrame[ np.random.choice[10, [3, 4]], columns=list['ABCD']].to_dict['r'] print[data] [{'A': 5, 'B': 0, 'C': 3, 'D': 3}, {'A': 7, 'B': 9, 'C': 3, 'D': 5}, {'A': 2, 'B': 4, 'C': 7, 'D': 6}]

This list consists of "records" with every keys present. This is the simplest case you could encounter.

# The following methods all produce the same output. pd.DataFrame[data] pd.DataFrame.from_dict[data] pd.DataFrame.from_records[data] A B C D 0 5 0 3 3 1 7 9 3 5 2 2 4 7 6

Word on Dictionary Orientations: orient='index'/'columns'

Before continuing, it is important to make the distinction between the different types of dictionary orientations, and support with pandas. There are two primary types: "columns", and "index".

orient='columns'
Dictionaries with the "columns" orientation will have their keys correspond to columns in the equivalent DataFrame.

For example, data above is in the "columns" orient.

data_c = [ {'A': 5, 'B': 0, 'C': 3, 'D': 3}, {'A': 7, 'B': 9, 'C': 3, 'D': 5}, {'A': 2, 'B': 4, 'C': 7, 'D': 6}] pd.DataFrame.from_dict[data_c, orient='columns'] A B C D 0 5 0 3 3 1 7 9 3 5 2 2 4 7 6

Note: If you are using pd.DataFrame.from_records, the orientation is assumed to be "columns" [you cannot specify otherwise], and the dictionaries will be loaded accordingly.

orient='index'
With this orient, keys are assumed to correspond to index values. This kind of data is best suited for pd.DataFrame.from_dict.

data_i ={ 0: {'A': 5, 'B': 0, 'C': 3, 'D': 3}, 1: {'A': 7, 'B': 9, 'C': 3, 'D': 5}, 2: {'A': 2, 'B': 4, 'C': 7, 'D': 6}} pd.DataFrame.from_dict[data_i, orient='index'] A B C D 0 5 0 3 3 1 7 9 3 5 2 2 4 7 6

This case is not considered in the OP, but is still useful to know.

Setting Custom Index

If you need a custom index on the resultant DataFrame, you can set it using the index=... argument.

pd.DataFrame[data, index=['a', 'b', 'c']] # pd.DataFrame.from_records[data, index=['a', 'b', 'c']] A B C D a 5 0 3 3 b 7 9 3 5 c 2 4 7 6

This is not supported by pd.DataFrame.from_dict.

Dealing with Missing Keys/Columns

All methods work out-of-the-box when handling dictionaries with missing keys/column values. For example,

data2 = [ {'A': 5, 'C': 3, 'D': 3}, {'A': 7, 'B': 9, 'F': 5}, {'B': 4, 'C': 7, 'E': 6}] # The methods below all produce the same output. pd.DataFrame[data2] pd.DataFrame.from_dict[data2] pd.DataFrame.from_records[data2] A B C D E F 0 5.0 NaN 3.0 3.0 NaN NaN 1 7.0 9.0 NaN NaN NaN 5.0 2 NaN 4.0 7.0 NaN 6.0 NaN

Reading Subset of Columns

"What if I don't want to read in every single column"? You can easily specify this using the columns=... parameter.

For example, from the example dictionary of data2 above, if you wanted to read only columns "A', 'D', and 'F', you can do so by passing a list:

pd.DataFrame[data2, columns=['A', 'D', 'F']] # pd.DataFrame.from_records[data2, columns=['A', 'D', 'F']] A D F 0 5.0 3.0 NaN 1 7.0 NaN 5.0 2 NaN NaN NaN

This is not supported by pd.DataFrame.from_dict with the default orient "columns".

pd.DataFrame.from_dict[data2, orient='columns', columns=['A', 'B']] ValueError: cannot use columns parameter with orient='columns'

Reading Subset of Rows

Not supported by any of these methods directly. You will have to iterate over your data and perform a reverse delete in-place as you iterate. For example, to extract only the 0th and 2nd rows from data2 above, you can use:

rows_to_select = {0, 2} for i in reversed[range[len[data2]]]: if i not in rows_to_select: del data2[i] pd.DataFrame[data2] # pd.DataFrame.from_dict[data2] # pd.DataFrame.from_records[data2] A B C D E 0 5.0 NaN 3 3.0 NaN 1 NaN 4.0 7 NaN 6.0

A strong, robust alternative to the methods outlined above is the json_normalize function which works with lists of dictionaries [records], and in addition can also handle nested dictionaries.

pd.json_normalize[data] A B C D 0 5 0 3 3 1 7 9 3 5 2 2 4 7 6 pd.json_normalize[data2] A B C D E 0 5.0 NaN 3 3.0 NaN 1 NaN 4.0 7 NaN 6.0

Again, keep in mind that the data passed to json_normalize needs to be in the list-of-dictionaries [records] format.

As mentioned, json_normalize can also handle nested dictionaries. Here's an example taken from the documentation.

data_nested = [ {'counties': [{'name': 'Dade', 'population': 12345}, {'name': 'Broward', 'population': 40000}, {'name': 'Palm Beach', 'population': 60000}], 'info': {'governor': 'Rick Scott'}, 'shortname': 'FL', 'state': 'Florida'}, {'counties': [{'name': 'Summit', 'population': 1234}, {'name': 'Cuyahoga', 'population': 1337}], 'info': {'governor': 'John Kasich'}, 'shortname': 'OH', 'state': 'Ohio'} ] pd.json_normalize[data_nested, record_path='counties', meta=['state', 'shortname', ['info', 'governor']]] name population state shortname info.governor 0 Dade 12345 Florida FL Rick Scott 1 Broward 40000 Florida FL Rick Scott 2 Palm Beach 60000 Florida FL Rick Scott 3 Summit 1234 Ohio OH John Kasich 4 Cuyahoga 1337 Ohio OH John Kasich

For more information on the meta and record_path arguments, check out the documentation.

Create a Pandas DataFrame from List of Dicts

Pandas DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. It is generally the most commonly used pandas object.

Pandas DataFrame can be created in multiple ways. Let’s discuss how to create a Pandas DataFrame from List of Dicts.

Code #1:

# Python code demonstrate how to create # Pandas DataFrame by lists of dicts. import pandas as pd # Initialise data to lists. data = [{'Geeks': 'dataframe', 'For': 'using', 'geeks': 'list'}, {'Geeks':10, 'For': 20, 'geeks': 30}] # Creates DataFrame. df = pd.DataFrame[data] # Print the data df

Output:

Code #2: With index

# Python code demonstrate how to create # Pandas DataFrame by lists of dicts. import pandas as pd # Initialise data to lists. data = [{'Geeks': 'dataframe', 'For': 'using', 'geeks': 'list'}, {'Geeks':10, 'For': 20, 'geeks': 30}] # Creates DataFrame. df = pd.DataFrame[data, index =['ind1', 'ind2']] # Print the data df

Output:

Code #3: With index and columns

# Python code demonstrate how to create # Pandas DataFrame by lists of dicts. import pandas as pd # Initialise data to lists. data = [{'Geeks': 'dataframe', 'For': 'using', 'geeks': 'list'}, {'Geeks':10, 'For': 20, 'geeks': 30}] # With two column indices, values same # as dictionary keys df1 = pd.DataFrame[data, index =['ind1', 'ind2'], columns =['Geeks', 'For']] # With two column indices with # one index with other name df2 = pd.DataFrame[data, index =['indx', 'indy']] # print for first data frame print [df1, "\n"] # Print for second DataFrame. print [df2]

Output:

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Create a Pandas DataFrame from List of Dicts

To convert your list of dicts to a pandas dataframe use the following methods:

1. pd.DataFrame[data]
2. pd.DataFrame.from_dict[data]
3. pd.DataFrame.from_records[data]

Depending on the structure and format of your data, there are situations where either all three methods work, or some work better than others, or some don't work at all.

Create Dataframe from list of dictionaries with default indexes

We can directly pass the list of dictionaries to the Dataframe constructor. It will return a Dataframe i.e.

import pandas as pd list_of_dict = [ {'Name': 'Shaun' , 'Age': 35, 'Marks': 91}, {'Name': 'Ritika', 'Age': 31, 'Marks': 87}, {'Name': 'Smriti', 'Age': 33, 'Marks': 78}, {'Name': 'Jacob' , 'Age': 23, 'Marks': 93}, ] # Create DataFrame from list of dictionaries df = pd.DataFrame[list_of_dict] print[df]

Output:

Name Age Marks 0 Shaun 35 91 1 Ritika 31 87 2 Smriti 33 78 3 Jacob 23 93

As all the dictionaries in the list had similar keys, so the keys became the column names. Then for each key, values of that key in all the dictionaries became the column values. As we didn’t provide any index argument, so dataframe has default indexes i.e. 0 to N-1.

But what if we want to have specific indexes too?

Create Dataframe from list of dicts with custom indexes

We can pass a list of indexes along with the list of dictionaries in the Dataframe constructor,

import pandas as pd list_of_dict = [ {'Name': 'Shaun' , 'Age': 35, 'Marks': 91}, {'Name': 'Ritika', 'Age': 31, 'Marks': 87}, {'Name': 'Smriti', 'Age': 33, 'Marks': 78}, {'Name': 'Jacob' , 'Age': 23, 'Marks': 93}, ] # Create Dataframe from list of dictionaries and # pass another list as index df = pd.DataFrame[list_of_dict, index=['a', 'b', 'c', 'd']] print[df]

Output:

Name Age Marks a Shaun 35 91 b Ritika 31 87 c Smriti 33 78 d Jacob 23 93

As all the dictionaries have similar keys, so the keys became the column names. Then for each key all the values associated with that key in all the dictionaries became the column values. Also, all the items from the index list were used as indexes in the dataframe.

“convert list of dict to dataframe” Code Answer’s

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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}

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

Note that the syntax of print [type[df]] was added at the bottom of the code toconfirm that we actually got a DataFrame.

Introduction to Importing Python Data to Pandas DataFrames

This tutorial will cover the basics of importing data from the internal Python lists and dictionaries into Pandas DataFrame structures. We will provide additional tutorials for importing data from external sources such as Microsoft Excel in later tutorials. The Pandas DataFrame structure provides a suite of tools for the manipulation and inspection of data. These DataFrames provide a powerful backbone to any data science project, and knowing how to create them from existing data is crucial.

Remember that any use of the Pandas module requires both installation of Pandas and Numpy 1 into the execution environment. Once the package is installed, all modules using the Pandas module will require the import statement import pandas at the start of the module.

This tutorial will show you how to convert dictionaries and lists to Pandas DataFrames. We’ll also explain how to create a Pandas DataFrame from a list of dicts and a list of lists. However your Python data is structured, this tutorial will show you how to convert them into a Pandas DataFrame.