Industrial Engineering Developer with Pandas Salary in 2024

Where is Pandas used?

Pandas Alternatives

Apache Arrow

A cross-language development platform for in-memory data, providing optimized data interchange for systems.
Example: Reading a CSV file into an Arrow Table.

import pyarrow.csv as pv
table = pv.read_csv('example.csv')

Pros:

• Language-agnostic data format

• Fast in-memory processing

• Seamless integration with other data tools

Cons:

• Less mature ecosystem compared to Pandas

• Smaller community support

• Learning curve for new users

Dask

Parallel computing library that scales Python analytics enabling efficiency in large datasets processing.
Example: Using Dask DataFrame to perform operations similar to Pandas.

import dask.dataframe as dd
df = dd.read_csv('large_dataset.csv')
df = df[df.column > 0]
result = df.compute()

Pros:

• Handles larger-than-memory datasets

• Parallel and distributed computing capabilities

• Compatible with Pandas API

Cons:

• Can be cumbersome for small datasets

• Requires understanding of parallelism

• Overhead for task scheduling

Polars

A Rust-powered DataFrame library with eager and lazy computations aimed at high performance & memory efficiency.
Example: Basic data manipulation in Polars.

import polars as pl
df = pl.read_csv('data.csv')
df = df.filter(pl.col("column") > 10)
df = df.select(["column", "other_column"])

Pros:

• Blazing-fast performance

• Memory-efficient

• Both eager and lazy execution

Cons:

• Still developing API

• Less extensive documentation

• Smaller user and contributor base

Quick Facts about Pandas

The Birth of a Data Giant

Imagine a world swamped with data but starving for tools to digest it. In steps Pandas, a Python powerhouse created by one Wes McKinney. The year was 2008, and McKinney, in a quest to analyze financial data, gives birth to this game-changer, becoming the data wrangler's knight in shining armor.

The Name Game

Don't let the cute animal association fool you! "PAN" from "Panel Data" and "DAS" from "Data Analysis" form the muscular acronym Pandas. It does not munch bamboo but devours colossal datasets with an insatiable appetite, and just like real pandas, it became an endangered species - almost extinct development-wise in 2015, but survived and thrived!

From Struggle to Supersonic Speeds

Forget the tortoise; this hare’s got nitro boosters! With Pandas 1.0.0 unleashed in January 2020, data manipulation turned supersonic, boasting stability and new features. One such feature, "Nullable Integer Data Type", is like finding a unicorn in a haystack. Here, watch it turn "N/A"s into zeros without breaking a sweat:

import pandas as pd

# Creating a DataFrame with missing values
df = pd.DataFrame({'col1': [1, 2, None]})

# Converting column to nullable integer type
df['col1'] = df['col1'].astype('Int64')

# Surprise! N/A turns into a soft zero, no fireworks.
print(df)

So there you have it, loyal subjects of Datatopia, a brief chronicle of Pandas—your gallant guardian against data disarray. Farewell, and may your dataframes never falter!

What is the difference between Junior, Middle, Senior and Expert Pandas developer?

Top 10 Pandas Related Tech

1. 
   

   Python

   
   

   This is the bread and butter, the cheese to your macaroni. Without Python, your Pandas are just bamboo-less bears. Python is the language that breathes life into Pandas, and it's as essential as coffee on a Monday morning. You've got to know your Python like the back of your hand, or, perhaps, like your favorite mug.

1. 
   

   NumPy

   
   

   It's like the Robin to your Batman. NumPy is the powerhouse for numerical computing in Python, and Pandas sits on its shoulders to reach the data manipulation cookies on the high shelf. Know NumPy to crunch numbers like you're munching on cereal.

   
   

   
   import numpy as np
   np_array = np.array([1, 2, 3, 4])
           

1. 
   

   Jupyter Notebook

   
   

   Think of it as your spellbook where magic happens. Jupyter Notebooks are where data storytelling unfolds, combining live code with narrative text. It's where your data analysis becomes as interactive as a game of "Whack-a-Mole."

   
   

   
   %matplotlib inline
   import pandas as pd
   df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
           

1. 
   

   SQLAlchemy

   
   

   This is your toolkit for database wizardry. SQLAlchemy lets you speak to databases in their native language, SQL, but through the comfort of Python scripts. It's like being able to converse with both animals and plants in an enchanted forest.

   
   

   
   from sqlalchemy import create_engine
   engine = create_engine('sqlite:///example.db')
           

1. 
   

   Matplotlib/Seaborn

   
   

   You'll be painting with data like Picasso with these. Matplotlib lets you craft fine traditional visualizations, while Seaborn spices them up with modern flavor. It's your art gallery of graphs and charts.

   
   

   
   import matplotlib.pyplot as plt
   import seaborn as sns
   data = sns.load_dataset("iris")
   sns.pairplot(data)
   plt.show()
           

1. 
   

   Excel/CSV Handling

   
   

   A data analyst without Excel skills is like a sushi chef who can't handle fish. Pandas love files in these formats like pandas love bamboo. Get ready to import, export, and twirl these files like a data ballerina. Bonus points if you can make Excel puns.

   
   

   
   df.to_csv('dataframe.csv', index=False)
   df_read = pd.read_csv('dataframe.csv')
           

1. 
   

   Dask

   
   

   If Pandas is an artist, Dask is the fancy new brush set. It scales your data science toolkit to the next level, allowing you to work with larger-than-memory datasets on your humble laptop, pretending it's the Hulk when it's really just Bruce Banner.

   
   

   
   import dask.dataframe as dd
   dask_df = dd.read_csv('large_dataset.csv')
           

1. 
   

   Git

   
   

   Ah, the time traveler's tool. Git lets you manage versions of your data projects like you manage your playlists, with the added bonus of easy collaboration. Commit, push, pull, and branch out your code like a wild data gardener.

1. 
   

   Scikit-Learn

   
   

   When you're ready to take that Pandas DataFrame and start making predictions, Scikit-Learn is your go-to. It's like turning your spreadsheet into a crystal ball, forecasting the mystical trends of the future.

   
   

   
   from sklearn.linear_model import LinearRegression
   model = LinearRegression()
   model.fit(X_train, y_train)
           

1. 
   

   APIs Integration

   
   

   Roll out the red carpet for APIs, the dignitaries that grace your data sets with fresh external info. Get comfortable pulling your weight with APIs, because when you do, you'll be the belle of the data ball, able to fetch data like Cinderella's fairy godmother.