Business Intelligence (BI) Developer with Pandas Salary in 2024

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Median Salary Expectations:

How statistics are calculated

We count how many offers each candidate received and for what salary. For example, if a Business Intelligence (BI) with Pandas with a salary of $4,500 received 10 offers, then we would count him 10 times. If there were no offers, then he would not get into the statistics either.

The graph column is the total number of offers. This is not the number of vacancies, but an indicator of the level of demand. The more offers there are, the more companies try to hire such a specialist. 5k+ includes candidates with salaries >= $5,000 and < $5,500.

Median Salary Expectation – the weighted average of the market offer in the selected specialization, that is, the most frequent job offers for the selected specialization received by candidates. We do not count accepted or rejected offers.

Where is Pandas used?




Data Wrangling in Chef's Kitchen

    • Like a master chef slicing and dicing ingredients, Pandas chops and stirs data into gourmet insights for data analysts.

Time-Traveling Financial Wizards

    • Pandas hops aboard the DeLorean, transforming historic stock prices into predictive alchemy, enchanting the wallets of investors.

Science Labs Get Schooled

    • In the petri dish of scientific research, Pandas is the bacteria that ferments raw numbers into the fine wine of knowledge.

Marketing Prophets and Their Crystal Balls

    • Marketing gurus gaze into the Pandas-powered crystal ball to foretell customer desires, crafting campaigns that resonate like a catchy jingle.

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')


    • Language-agnostic data format


    • Fast in-memory processing


    • Seamless integration with other data tools


    • Less mature ecosystem compared to Pandas


    • Smaller community support


    • Learning curve for new users


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()


    • Handles larger-than-memory datasets


    • Parallel and distributed computing capabilities


    • Compatible with Pandas API


    • Can be cumbersome for small datasets


    • Requires understanding of parallelism


    • Overhead for task scheduling


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 =["column", "other_column"])


    • Blazing-fast performance


    • Memory-efficient


    • Both eager and lazy execution


    • 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.

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?

Seniority NameYears of ExperienceAverage Salary (USD/year)Responsibilities & Activities
Junior0-2$50,000 - $70,000

    • Data cleaning and preparation using pandas basic functionalities.

    • Assisting in data analysis under supervision.

    • Writing simple data manipulation scripts.

Middle2-5$70,000 - $95,000

    • Developing data pipelines and processing routines.

    • Optimizing data operations for performance improvements.

    • Participating in code reviews and optimization of pandas code.

Senior5+$95,000 - $120,000

    • Designing and implementing complex data analysis projects.

    • Leading data modeling and architecture efforts.

    • Mentoring junior and middle developers.

Expert/Team Lead8+$120,000 - $150,000+

    • Setting project timelines and milestones.

    • Overseeing multiple data projects and ensuring best practices.

    • Strategizing on data acquisition and processing at scale.


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")


    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(), 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.


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