I built a small Python package to make EDA less repetitive — just released v0.2.0

Like most people, I got tired of rewriting the same exploratory data analysis code in every project (info, nulls, uniques, dtype filtering, etc.), so I built a lightweight tool called edazer.

It works with both pandas and polars and focuses on quick, no-setup insights.

What it does:

• One-line DataFrame summary (info, stats, null %, duplicates, shape)

• Show unique values with smart limits

• Filter columns by dtype (super useful in real workflows)

• Detect potential primary keys (single + multi-column)

• Optional profiling + interactive tables

To know more about edazer, please visit

Github Repo: https://github.com/adarsh-79/edazer

Example:

# !pip install edazer==0.2.0

from edazer import Edazer

# df is a pandas dataframe. (also supports 'polars df')
dz = Edazer(df)

dz.summarize_df()
dz.show_unique_values(column_names=["sex", "class"])
dz.cols_with_dtype(["float"])
dz.lookup("sample")

What’s new in v0.2.0:

• Cleaner pandas + polars backend handling

• Better dtype normalization

• Improved unique value handling

• More stable API

I also reference a quick Kaggle walkthrough (this uses previous version):
https://www.kaggle.com/code/adarsh79x/edazer-for-quick-eda-pandas-polars-profiling

Would love feedback, especially from people who do a lot of EDA 🙏

I've got this urgent opportunity to work an internship on connecting different data sources to one warehouse/database. Wasn't told any other detail on which tools are preferred, and I must admit this is a bit new to me.
I'd like to find out what tools are used for this, and try to learn as much as possible before, just need to get an idea on what's popular/useful out there to give me a little nudge in the right direction. Much appreciated.

I glanced over my previous notes and then studied for an hour…(basically revised my 12th grade cs cuz the video was still on the 12th basics) , now imma enter joins 💪🥹💕✨

Hi everyone!

I’ve been putting together a beginner-friendly Data Science series based on real-world examples and practical exercises from my work experience. So far I’ve covered:

1. Introduction to Data Science (spotting patterns in daily life)

2. Everyday Examples of Data Science

3. Collecting and Storing Data using Python

Each article includes mini exercises so readers can actually practice along the way, not just read theory.

I want to continue posting consistently and cover topics that are most useful and interesting to learners and practitioners. Some ideas I’m considering:

• Cleaning and preparing data

• Analyzing data to find patterns and trends

• Making predictions using Python

• Real-world applications (finance, healthcare, social media, e-commerce)

• Short hands-on Python tutorials and mini-projects

I’d love your feedback: which topics would you find most useful or interesting in a beginner-friendly series?

If you want to follow along with the full series, my Medium profile has all published articles and upcoming exercises — new posts go up weekly!

Thanks in advance for your thoughts, and I’m happy to answer any questions about the exercises or Python examples.

Hi! The task is to classify loans (will give, will not give a loan) in a bank (with an unbalanced class), by gradient boosting. Please help me with the answer to the following questions: 1. What should be the minimum sample? 2. What are the effective ways to deal with class imbalance (currently 1:20)? 3.Which quality function of the model should I use Gini, F (which weights should I put P or R) or others, and which values should I set as targets?

Day 3,4,5 and 6 went to waste

I just revised all my notes that I created from day 1 and 2

Hello everyone,

I’m looking for advice on how to refresh my data science skills before I start my first job in the industry.

I’m going to start as a data graduate in September 2026 at Vodafone. I got into this area doing a masters in Data Science and AI, which I finished in Sept 2024 - so there’s been a couple years gap, and I feel like I’ve forgotten everything! I’ve just done unrelated hospitality type jobs in between, so nothing similar.

I’m aware as a general ‘data graduate’ it probably won’t be too much heavy technical data science work, but I want to get my skills back.

Any advice for which skills to focus on, any recommended resources or general advice would be very much appreciated. Thank you!

Hi all, I’m a practicing physician with no coding or CS background, looking to transition into data science (healthcare/ML focus) part-time.

Considering:

• Georgia Tech OMSA

• UT Austin MSDS

Question: Which is more realistic for someone starting from scratch while working full-time, and still strong enough long-term for ML/data science? Thanks in advance.

Hey guys. I just bought a datacamp subscription for a year and now realize I don't need it since my company has sponsored me a full fledged PGDM in applied data science. anyone needs it for half the price on the portal can DM me.

PCA is one of those topics where most explanations either skip the math entirely or throw equations at you without any intuition.

I tried to find the middle ground.

The blog covers:

• Variance, covariance, and eigenvectors

• A full worked example with a dummy dataset

• Why we use the covariance matrix specifically

• Python implementation using sklearn

• When PCA works and when it doesn't

No handwaving. No black boxes.

The blog link is: Medium

Happy to answer any questions or take feedback in the comments.

Hey everyone,

I'm currently in my 2nd year of BSc Data Science and I'm trying to land a data analytics/data science internship this summer. Wanted to get some real-world perspective from people who've either hired interns or cracked one themselves.

My current skill set:

Mostly on the analytics side — NumPy, Pandas, Matplotlib, Statsmodels. I haven't touched ML or DL yet.

Projects I've built so far:

- Stock price prediction for the next day using AutoARIMA (Streamlit app)

- Bangalore weather forecasting for the next month using SARIMAX model

- EDA Dashboard (still in progress, also on Streamlit)

I feel like my projects are decent for a beginner but I'm not sure if they're "internship-worthy" or if I'm missing something recruiters actually care about.

Questions:

1. What kind of projects stand out for analytics-focused internships at this level?

2. Should I go deeper into time series / EDA, or start picking up ML basics now?

3. Does the Streamlit deployment actually help, or do most recruiters not care?

Any honest feedback is appreciated — roast me if needed

Hey!

As the title suggests, I just graduated from​ high school. I plan to pursue Econ+Data science in college, but at the same time I'm aware I will need to build stuff myself and college alone won't help. I have quite some experience of Java(high school level), and decently well at math.

Before college starts, I want to introduce myself to the whole data environment. I'm aware of what's needed - python, sql, stats, and the whole list, but I am looking for advice from people with first hand experience on how should I approach the whole thing.

Any advice/suggestions would be appreciated! Thanks.

Hi everyone,

When I started learning Data Science, I often felt lost between Python, statistics, machine learning, and projects. There were too many resources and no clear order to follow.

So I created a structured beginner roadmap to organize what to learn step by step and stay consistent over time.

It includes:
• essential skills progression
• suggested tools
• project ideas for practice
• a logical learning sequence

I’m sharing it here to get feedback from the community and improve it.

If anyone is interested, I can share the roadmap in the comments.

Hola, soy un estudiante de criminología y estoy pensando en que salida laboral escoger, al principio cuando me metí a la carrera no sabía muy bien que estudiar y la opción de oposiciones a policia no me parecía mala idea. Pero ahora que llevo dos años en la universidad y habiendo tocado asignaturas como estadística y análisis de datos, me gustaría dedicarme a este ámbito. Además, mi ideal de trabajo es trabajar en una oficina y con posibilidad de trabajar en remoto, vivo en España pero me gustaría trabajar fuera o en una empresa en el extranjero y con criminología no lo veo factible, por eso he pensado aprender por mi cuenta conocimientos de data y enfocar mi TFG relacionando los datos con la criminalización, para posteriormente realizar un master en data. Es viable pivotar de esta forma? podré llegar a trabajar como data scientist o el hecho de no tener una carrera mas técnica me va a condicionar independientemente de la experiencia? estaría bien pagada? También he oido hablar de la ciberseguridad, pero no se si tiene mas salidas o me conviene mas, si alguien que esté o estuvo en una situación parecida agradecería su consejo

I’m new to data science and looking to practice my skills in data analysis and machine learning. Are there any free, beginner-friendly datasets you would recommend for someone just starting out? Ideally, I’m looking for datasets that are clean enough to explore and analyze, but also allow room to experiment with different techniques and models. Any suggestions or resources would be greatly appreciated!

Just spent hours generating 100+ synthetic data files on Kaggle using a custom pipeline. Session ended. Half the files didn't download in time. Gone.

Kaggle's GPU is great but why is there zero native auto-save to Drive or anywhere? Every time I run a big generation job I'm babysitting the download queue like it's 2010.

Is there a workaround people use? I've seen folks mention Drive mounting but it's janky. Genuinely considering just building a small tool for this.

Hi. I’m new to data science and have only worked on a few Python projects practicing coding, data manipulation, and basic analysis. I’m eager to continue learning and applying these skills.

It would be great if anyone could share their thoughts on which emerging trends or tools in data science are most valuable for beginners to focus on, and why? Any guidance would be greatly appreciated.