How to create versatile Pandas DataFrames from dictionaries of series in Python ? – Solved

Python Pandas @

Creating DataFrames from dictionaries of series offers flexibility and efficiency, especially when dealing with complex data structures. It allows for the combination of series with potentially different lengths and data types into a coherent tabular structure.

Guide to Creating DataFrame from Dictionary of Series

Preparing the Data

First, let’s create some series with real data. We’ll use names as indices and other information as data.

Example Series:

import pandas as pd
# Creating series learning @
sachin_series = pd.Series(data={'Age': 32, 'City': 'Mumbai', 'Occupation': 'Engineer'})
manju_series = pd.Series(data={'Age': 29, 'City': 'Bangalore'})
ram_series = pd.Series(data={'Age': 35, 'City': 'Chennai', 'Occupation': 'Doctor', 'Salary': 150000})
raju_series = pd.Series(data={'Age': 40, 'City': 'Delhi'})
david_series = pd.Series(data={'Age': 28, 'City': 'New York', 'Salary': 85000})
wilson_series = pd.Series(data={'Age': 33, 'City': 'San Francisco', 'Occupation': 'Architect'})

Creating the DataFrame

Next, we create a dictionary of these series and use it to form a DataFrame.

Example of Creating DataFrame:

# Dictionary of series
data_dict = {'Sachin': sachin_series, 'Manju': manju_series, 'Ram': ram_series, 
             'Raju': raju_series, 'David': david_series, 'Wilson': wilson_series}
# Creating DataFrame
df = pd.DataFrame(data_dict)
# Transposing to get names as rows
df = df.T


	Age	City	Occupation	Salary
Sachin	32	Mumbai	Engineer	NaN
Manju	29	Bangalore	NaN	NaN
Ram	35	Chennai	Doctor	150000
Raju	40	Delhi	NaN	NaN
David	28	New York	NaN	85000
Wilson	33	San Francisco	Architect	NaN

Understanding the Resulting DataFrame

The DataFrame df will have names as row indices and the keys of series (like ‘Age’, ‘City’, etc.) as columns. Missing values are automatically handled and represented as NaN.

Benefits of This Approach

  • Flexibility in Data Structure: Handles series with different lengths and missing values gracefully.
  • Ease of Manipulation: Easy to add or remove data, making it highly dynamic.
  • Simplifies Complex Data Aggregation: Ideal for combining series representing different aspects of data into a single structure.

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Author: user