How to Use Matplotlib for Data Visualization?

TL;DR: Start with basic plots using plt.plot(), explore object-oriented interfaces like fig, ax = plt.subplots(), and consider integrating Seaborn for enhanced functionality.

Basic Plotting with Matplotlib

Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. The simplest way to create plots is by using plt.plot() for line graphs. This function is akin to plotting functions in MATLAB and is bound to the current figure and axis [1:11]. For more complex layouts, use fig, ax = plt.subplots() which allows multiple plots within one figure [1:9].

Handling Large Datasets

While Matplotlib can handle large datasets, some users have experienced performance issues with very large data points [1:4]. It's recommended to aggregate or sample your data if you're dealing with millions of points [1:5]. Alternatively, libraries like Bokeh or Plotly are suggested for handling larger datasets due to their interactivity and scalability [1:10], [5:6].

Choosing Between Libraries

Matplotlib is often used alongside Seaborn, which simplifies many statistical plots and offers built-in functionalities that require less code compared to raw Matplotlib [5:4]. Seaborn is particularly useful for statistical data visualization and can be a good starting point for beginners [3:1], [5:12]. For interactive dashboards, Plotly or Tableau might be preferable [3:3], [4:7].

Customization and Flexibility

Matplotlib provides extensive customization options, allowing pixel-level control over your visualizations [4:6]. However, this flexibility comes at the cost of increased complexity and potential for more code. Users who prefer GUI-based tools may find Tableau easier for quick and interactive visualizations [4:10].

Additional Resources

For comprehensive learning, several tutorials and resources are recommended:

Jupyter Notebook tutorial on Matplotlib [1:1]
Effective Matplotlib guide [1:2]

These resources provide detailed insights into Matplotlib's capabilities and how to effectively use it for various types of data visualization tasks.

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POST SUMMARY • [1]

Summarize

A complete tutorial on data visualization with Python using Matplotlib

Posted by pylenin · in r/Python · 6 years ago

451 upvotes on reddit

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chaostools · 6 years ago

Newbie here. I am always confused about using plt.plot(...) Or fig, ax = plt.subplots() and then ax.plot(..) or just using dataframe.plot()/series.plot() or using seaborn. It would be great if someone covered every related function/ method to make plots. Its kinda tough searching for them, since i always end up with a solution solving problem with a different method than what i chose

I Don't have time now to watch these vids, but would surely watch the playlist when my exams get over.

7 upvotes on reddit

[deleted] · 6 years ago

Do yourself a favor and check out these two links as they are probably the best resources for learning MATPLOTLIB:

link1

link2

5 upvotes on reddit

BDube_Lensman · 6 years ago

plt.plot is an analog to a function in matlab. Don't have history with matlab? Don't use plt.plot where possible. The plot function creates an "artist" which is a fancy way of saying a line, polygon, collection of dots, or other thing that is a visual element of a figure. When you use plt.plot, it is bound to whatever gcf ("get current figure") and gca ("get current axis") points to. In a short script, this is pretty deterministic (only one figure) but in a more complicated rendering workflow this can cause unexpected behavior.

plt.subplots is a way of generating a figure and axis/axes. ax.plot is identical to plt.plot, but the result is bound to ax explicitly.

df.plot, etc, are just plotting code that wraps matplotlib built into pandas. These functions usually return the axis they drew on, and also take an axis to draw on as an argument (ax). Same for seaborn.

Generally, if you have a statistically-oriented plotting task, you can comfortably get by just with the tools in pandas or seaborn (pick one, seaborn is somewhat preferred) and ignore the existence of matplotlib altogether, except when it comes time to save a figure.

If you do more scientific visualizations, or otherwise draw stuff more complicated than what seaborn, etc, can do out of the box, you will end up using matplotlib directly more often.

People have mixed views, but when you want to alter axes limits, labels, and/or titles I like to use ax.set, you can do something like

ax = sns.boxplot(...)
ax.set(xlim=(1,2), xlabel='foo',
       ylim=(3,4), ylabel='bar',
       title='baz')

which is a style I find very clean.

9 upvotes on reddit

chaostools · 6 years ago

Yepp i also made code like the one you said in the end .

I am currently in the learning phase (got myself a free scholarship) and in the videos they taught a little about the pandas' plot function , and a relatively more about matplotlib and the plt.plot() functions.

However ,am always trying to find a swiss knife kinda thing out of these methods like, "which of them would help me customize the most in my graphs?" And weirdly its always a mix of 2-3 libraries than a single silver bullet.

Like recently i had to plot a time series. When plotted with plt.plot (matplotlib function) or ax.plot(), the time was in terms of daily date strings (around 400) and dates showed like a big black blob. So i had to use pd.to_datetime(..) (pandas function ) to get somewhat better graph. But that didn't looked satisfying, so i removed the pandas function ,created a manual loop, filtered the dates and then used as xticks. But suddenly i came across a stackoverflow answer saying you can just plot using series.plot() without a black blob , and that was it, it really handled all the dates without any conversions and showed a plot in terms of months

You see the problem? Its always like that. I always thought that pyplot is the most advanced and to-go solution for making graphs. But turns out it also isn't the best bet and sometimes it can't provide a good graph without manual tweaks or tweaks from other libraries. Also, if series.plot / dataframe.plot are merely wrappers for matplot, how are they able to make better graphs without any data tweaking?

1 upvotes on reddit

pylenin · OP · 6 years ago

Hey there.... No worries!! Watch it on your free time.

The difference amount the 3 is in your usage.plt.plot only allows you to plot one figure. However ax.plot allows you to plot multiple plots in one figure. If you just want to plot one figure, you don't need to use ax.plot.

Seaborn is a python library that is built using matplotlib to visualize complicated data. Sometimes if you do that using matplotlib, you will need to write a lot of code. Seaborn simplifies that by providing you with some built-in methods. So it all depends on what you want. I will make some tutorials on seaborn very soon.

Btw, if you go through the tutorials, you will exactly know the difference! So make sure you subscribe so that you can come back whenever you want.

2 upvotes on reddit

shampoopoo2 · 6 years ago

Just a bit of advice: Matplotlib is good... if your data is small. I switched to Bokeh because Matplotlib couldn't handle plots with >1000 data points.

3 upvotes on reddit

Broric · 6 years ago

? Of course it can. I've done plots with millions of data points. Obviously with that many points you need to start thinking about what you are trying to achieve and whether aggregation can convey the message in a simple way but matplotlib can technically handle it fine.

18 upvotes on reddit

woooden · 6 years ago

Same, I use matplotlib in a tool at work that visualizes multiple lines with thousands of points and matplotlib is in no way a bottleneck.

1 upvotes on reddit

shampoopoo2 · 6 years ago

Interesting. Could have been the type of plot or the data, but I know I'm not the only one who has had problems with matplotlib and large data... thats how i found bokeh.

1 upvotes on reddit

pylenin · OP · 6 years ago

Subplots video is uploading ! Styles video is in progress !! Axes and figures and object oriented APIs are advanced level. So they would be shown while doing a financial data analysis experiment.

12 upvotes on reddit

[deleted] · 6 years ago

I would check out these two links as they are probably the best resources for learning MATPLOTLIB and may give you some ideas for your videos:

link1

link2

9 upvotes on reddit

BlueDevilStats · 6 years ago

I wouldn't say better, but there are several packages in R that make visualization quite easy. The primary package that comes to mind is ggplot2 (available in python as ggplot although I haven't used it).

R can be a bit counter-intuitive at first, but like any tool it has its strengths. Hadley Wickham has made tremendous improvements via the tidyverse.

6 upvotes on reddit

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r/data_irl • [2]

Summarize

A complete tutorial on data visualization using m] Matplotlib in Python(data_irl)

Posted by pylenin · in r/data_irl · 6 years ago

youtube.com

28 upvotes on reddit

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reddirocco · 6 years ago

Kewl thanks

2 upvotes on reddit

pylenin · OP · 6 years ago

My pleasure !!!

1 upvotes on reddit

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r/datascience • [3]

Summarize

What data visualization library should I use?

Posted by UGotKatoyed · in r/datascience · 3 years ago

Context: I'm learning data science, I use python. For now, only notebooks but I'm thinking about making my own portfolio site in flask at some point. Although that may not happen.

During my journey so far, I've seen authors using matplotlib, seaborn, plotly, holoViews... And now I'm studying a rather academic book where the authors are using ggplot from plotline library (I guess because they are more familiar with R)...

I understand there's no obvious right answer but I still need to decide which one I should invest the most time in to start with. And I have limited information to do that. I've seen rather old discussions about the same topic in this sub but given how fast things are moving, I thought it could be interesting to hear some fresh opinions from you guys.

Thanks!

8 upvotes on reddit

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-xylon · 3 years ago

I have tried matplotlib, plotly/dash, holoviews/hvplot/panel and altair. And I must say: matplotlib is king. Plus, there's mpld3, that supposedly translates matplotlib code to D3.js code (but I haven't tried it).

6 upvotes on reddit

technocal · 3 years ago

Here’s what I use.

Polished and intended for others: Plotly or Tableau

Exploratory analysis: seaborn

Quick ad-hoc stuff: matplotlib

20 upvotes on reddit

[deleted] · 3 years ago

Best answer tbh

4 upvotes on reddit

Rough-Pumpkin-6278 · 3 years ago

Second this view!

1 upvotes on reddit

nichtgefunden · 3 years ago

I like ggplot and learned that one first but matplotlib and seaborn are super common. I know those too. I'd probably start with matplotlib and add on seaborn. I've used plotly for Dash dashboards.

IDK I guess I tend to think that the plotting library isn't as important as understanding how to properly display data. So just pick one.

7 upvotes on reddit

suicidalpasta · 3 years ago

plotly is fantastic for interactivity, and if you learn a bit about dash (a library for dashboards) you can do some pretty cool things. It can get messy when working at a low level directly with the graph opjects api though.

11 upvotes on reddit

BlaseRaptor544 · 3 years ago

I typically see matplotlib or seaborn in use in my current company

18 upvotes on reddit

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r/datascience • [4]

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Thoughts on MatPlotLib Python library vs Tableau data visualization?

Posted by dssdddd · in r/datascience · 5 years ago

What are your opinions for using either? A lot of the times I avoid using Matplotlib because I can just display data faster with tableau (theres so much customization). And I end up writing less code as a result. In what use case will it be better to use Matplotlib instead? 3D models?

125 upvotes on reddit

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HypEconomist · 5 years ago

Once, when I have been interviewed, the recruiter couldn't repeat "mat-plot-lib", she've been saying: "this lib"))

2 upvotes on reddit

[deleted] · 5 years ago

that plotting library

1 upvotes on reddit

canbooo · 5 years ago

For me they have different use cases. If I am making a web app, for public domain or company use, Tableau/Plotly are my go to tools. For general purpose data visualization/exploration, i prefer matlab/seaborn/pandas plots. Mostly because I think they are faster to produce/iterate and little interactivity is required for such a plot. Edit: Spelling.

81 upvotes on reddit

ratterstinkle · 5 years ago

What are the limitations of Tableau compared to Matplotlib?

3 upvotes on reddit

flextrek_whipsnake · 5 years ago

Tableau shines when you need visualizations that are interactive. You can create a dashboard with a bunch of filters and nice looking tooltips in like 5 minutes. There are open source alternatives, but Tableau gives you more out of the box than any of them. Of course Tableau becomes a god damn nightmare as soon as you stray even a little bit from whatever they've implemented for you. It's also a pain to version control.

Personally, I stick to ggplot/matplotlib for most things and use Tableau when I need user interactivity. I know some data scientists I respect a lot who have abandoned ggplot/matplotlib and use Tableau for pretty much everything.

29 upvotes on reddit

matbau · 5 years ago

I like what you said in your last part. I am a civil engineer who is learning python pandas and matplotlip to make my data analysis of the ready mix concrete production where I am currently working (not enough budget to hire a proper data analyst, third world.country issues). Every single data is managed with Excel files and I am in search for a more powerfull tool for data analysis and data visualization, and a dashboard for my bosses to present the data. I managed to make a dashboard in excel but I feel its lacking something, and it doesn't feel the proper way neither.

The part of user interactivity is what attracted me the most. Do you recommend me to start using (learning) tableau for what I am aiming for?

6 upvotes on reddit

JBalloonist · 5 years ago

Also take a look at Altair. Better looking than Matplotlib (at least without customization). Very similar to how ggolot2 works in R.

2 upvotes on reddit

Treemosher · 5 years ago

I just started using Tableau after sucking with Matplotlib and wish I had tried it sooner. Tableau is great when you want to give someone the ability to explore their own questions on a dashboard with filters.

1 upvotes on reddit

Astrohunter · 5 years ago

Ask yourself this: how much flexibility and creative control do you want over your visualisations? If you want to retain full control, choose Matplotlib. Just know that full control also usually means more work involved to get a basic result. But you're in total control on a pixel level. In Tableau you're much more constrained. But Tableau can also be more fun to use because of the GUI.

28 upvotes on reddit

AuspiciousApple · 5 years ago

Laughs in plt.style.use('ggplot')

9 upvotes on reddit

[deleted] · 5 years ago

ggplot can do a lot more than matplotlib right? Faceting, linear fitting , binning and plotting aggregates etc are single line statements in ggplot. I don't know if all that is possible in matplotlib/seaborn with that much ease. Plotting with ggplot is so much easier than anything I've encountered on python. You spend less time fiddling with labels ticks etc..

7 upvotes on reddit

noblesavage81 · 5 years ago

I use PowerBI not Tableau, but as far as I’ve heard they’re pretty similar. I disagree that it’s just “click and point”. The hard part is figuring out how to structure the data and create a data model that properly joins multiple datasets, then figuring out how to use the visualization language (in my case Dax) to structure the data to create the graphs you want. On some level it can just be dragging components around, but if you want to make anything one level deeper it certainly becomes more complex (and annoying). I have a cs background, so having all of the flexibility to structure the data in a language I’m really familiar with is easier for me. Then I just send in the plot parameters. So I’d argue tools like Tableau aren’t inherently easier than matplotlib, people might think so just because the latter is backed by a real programming language.

13 upvotes on reddit

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r/datascience • [5]

Summarize

What's your favorite data visualization tool for Python and why ?

Posted by 7Seas_ofRyhme · in r/datascience · 3 years ago

My current favorite ones are Seaborn and Plotly. What are your usual go-to when it comes to plotting basic charts and complex ones ?

Thanks

291 upvotes on reddit

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space__sloth · 3 years ago

Agree. First I use pandas plot to quickly prototype, then I rewrite using altair. However, for image based plots I still use matplotlib.

1 upvotes on reddit

prosocialbehavior · 3 years ago

Have you ever tried using D3.js or Plot? It is JavaScript but if you are rewrite anyways these libraries can be even more flexible than Altair in my opinion. But I do a lot of front end work so I like using JavaScript.

1 upvotes on reddit

prosocialbehavior · 3 years ago

Yeah Altair (Vega-Lite.js) just extended Wilkinson’s Grammar of Graphics to include Interactivity.

https://idl.cs.washington.edu/files/2017-VegaLite-InfoVis.pdf

I also find this way of programming charts very intuitive.

5 upvotes on reddit

mimprocesstech · 3 years ago

I dunno. Matplotlib.pyplot is a pain, but it's the only one I know so far and I haven't run into anything I couldn't do with it yet.

123 upvotes on reddit

DuckSaxaphone · 3 years ago

Give seaborn a go!

Seaborn is really just a set of convenience functions which make pyplot plots for you. If you're using dataframes and like their style, then it's basically just making pyplot easier to use.

I'm a pyplot fan but often find that seaborn will either do everything I was going to do very easily or at least be a good jumping off point, giving me a plot I can do final tweaks too.

87 upvotes on reddit

mayankkaizen · 3 years ago

Your comment somehow underestimated the capabilities of Seaborn. I would call Seaborn statisticians' plotting library because almost every Seaborn plot API has statistical functionalities built in. Matplotlib is great for raw plotting. Seaborn offers much more.

For example -

x = [7.5, 7.5, 17.5]
y = [393.198, 351.352, 351.352]


plt.plot(x,y)

sns.lineplot(x=x,y=y)

They both are similar but plot different things because Seaborn has much more functionalities and depending on the parameters will plot differently.

Another great thing about Seaborn is that it plays very nicely with Pandas dataframes.

11 upvotes on reddit

Aesthetically · 3 years ago

I'm going to check this out, thanks. I have struggled to "get" matplotlib in a way that makes it easy to integrate. I haven't practiced too much with it since I have data visualization platforms that my org likes to use (and Jupyter notebooks scare people over 45)

3 upvotes on reddit

Biogeopaleochem · 3 years ago

Don’t even get me started on matplotlib. For loops just to get all the data on the plot at once? Like I get it but it’s super clunky if you’ve never used it before. That said there are a lot of nice example on the website you can just paste your code into which is what I end up doing most of the time. Seaborn is nice though.

3 upvotes on reddit

mimprocesstech · 3 years ago

I've never used a for loop with matplotlib. I'm usually working with a gui or an excel workbook though.

1 upvotes on reddit

Adorable-Giraffe5754 · 3 years ago

It is!

Only drawback imo is that it gets really slow when a lot of datapoints are visualized.

As my research group works mainly with large time series data, we developed an extension that solves this problem.

For large sequences (scatter plots) Plotly Resampler enables to visualize tons of datapoints (through adaptive resampling).

Sorry for the shilling, but plotly with plotly-resampler truly is my daily driver :)

8 upvotes on reddit

thebochman · 3 years ago

This is the answer, I tried learning matplotlib and seaborn but the end result is just not as nice as something I can whip up in tableau public

1 upvotes on reddit

neb2357 · 3 years ago

I'm also an R trained scientist learning python and God I miss ggplot2. Matplotlib is as capable as ggplot2, but it's not as intuitive.

7 upvotes on reddit

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r/Cinema4D • [6]

Summarize

Any resources on how to use C4D for data visualization?

Posted by buffbuf · in r/Cinema4D · 5 years ago

I happen to work at a genomics lab and was wondering if there are any known examples of applying C4D to representing data, much like the graphs, heat maps, plots etc one would see in biomedical journals but in 3 dimensions and specifically rendered in C4D.

1 upvotes on reddit

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b_marl · 5 years ago

I only ever did very primitive stuff using existing polygons or splines and control them via imported csv values (via structure manager), but that was before Python. Now you should be able to read in any ascii data using python and create points for curves from it.A quick google on the topic found these
http://www.plugincafe.com/forum/forum_posts.asp?TID=6622
https://stackoverflow.com/questions/51914080/how-to-read-massive-csv-files-with-python-inside-cinema4d

2 upvotes on reddit

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r/Python • [7]

Summarize

Dev'ing an app to help visualize data from any matplotlib figure

Posted by professormunchies · in r/Python · 5 years ago

gfycat.com

5027 upvotes on reddit

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jampk24 · 5 years ago

How does it know which points are higher or lower? Do you have to give it the color scale?

49 upvotes on reddit

professormunchies · OP · 5 years ago

The RGB information from a photo is first converted into a hue. The hue range is normalized between 0 and 1 and used to displace the vertices of a mesh. The height axis (z) is not to scale with the x&y but I'll had functionality to customize that. I have ideas for a CNN to automatically detect the color values and create a normalized image...that's still in my head though... Otherwise you have to specify which color scale inversion you want (viridis, jet, binary, etc..)

91 upvotes on reddit

sh0rtwave · 5 years ago

Oh nice. So you could integrate with like, d3-scale-chromatic on the other side of it.

1 upvotes on reddit

danielcs2009 · 5 years ago

My calculus teacher would die for this

26 upvotes on reddit

dtbrough · 5 years ago

Any chance of sharing the source code? Would love to have a play with that myself.

177 upvotes on reddit

professormunchies · OP · 5 years ago

You can follow the dev progress here: https://github.com/pearsonkyle/Data-VisualizAR

I'll open source the app in 2-3 weeks or so...I need to polish the color-map inversion because it only works for rainbow colormaps at the moment.

232 upvotes on reddit

dtbrough · 5 years ago

Nice. I’ll follow the repo. Keep up the excellent work.

46 upvotes on reddit

caks · 5 years ago

!RemindMe 3 weeks

13 upvotes on reddit

bcky429 · 5 years ago

This would be such a good teaching tool.

2 upvotes on reddit

razin99 · 5 years ago

One of the coolest project I've seen. Can you give us a tldr on how you did it and with what framework/library?

62 upvotes on reddit

professormunchies · OP · 5 years ago

The app is built with ARFoundation in Unity3D. ARfoundation is a higher level interface for the hardware specific AR libraries on android and iOS, basically code once and deploy to both platforms. The mesh is procedurally generated using a shader I wrote to displace the vertices based on the color

58 upvotes on reddit

_whatevs_ · 5 years ago

Something something computers. Got it, thanks!

Seriously, I'm not competent to judge your work, but goddamn the results are impressive!

14 upvotes on reddit

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r/CrossView • [8]

Summarize

Anyone here do data visualization? I wrote a Matplotlib add-on to see your plots in 3D by making stereograms and anaglyphs!

Posted by scottshambaugh · in r/CrossView · 1 year ago

reddit.com

52 upvotes on reddit

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scottshambaugh · OP · 1 year ago

There's a lot of awesome stereograms here and on /r/MagicEye made for fun, but I've never seen any made for practical purposes. This is a huge mistake! Stereographic images can significantly enhance the interpretability of 3D data by leveraging human binocular vision. Instead of looking at a flat projection on a page, stereograms give us “3D glasses” for 2D data with just our eyes.

I’ve been on a data visualization kick recently, working to overhaul Matplotlib’s 3D plotting capabilities with bug fixes and new features. But the end result of those is still a flattened image – I wanted to actually see my plots in 3D space. So, I made a Matplotlib extension called mpl_stereo to do just that! Along with stereograms, there is also support for making anaglyphs that can be used with regular old red-blue 3D glasses for those who don't know the stereogram viewing technique, as well as wiggle stereograms for people with neither.

Check it out on GitHub! All the info on using the library is in the readme there. Note that the docs there all use parallel view, but when generating your own it's easy to flip to cross view.

8 upvotes on reddit

romwell · 1 year ago

Thanks! The usage is as simple as it gets, super intuitive and ergonomic!

Check it out on GitHub! All the info on using the library is in the readme there. Note that the docs there all use parallel view, but when generating your own it's easy to flip to cross view.

TL;DR: use negative ipd (inter-pupillary distance) to generate cross-view instead of parallel-view.

2 upvotes on reddit

scottshambaugh · OP · 1 year ago

Thanks! I tried to make it as easy to use as possible so that means a lot to hear!

1 upvotes on reddit

FelipeReigosa · 1 year ago

...but I've never seen any made for practical purposes. This is a huge mistake!

The only I've seen is a program I was using years ago, I don't even remember the name, where you could see the structure of proteins as a stereogram, it was incredible since proteins have a very interesting shape. I agree with you, this should be used more.

1 upvotes on reddit

[deleted] · 1 year ago

awesome! Now, if only I could prompt my peers to do the cross-eye technique..

2 upvotes on reddit

romwell · 1 year ago

Absolutely glorious! Thank you!

2 upvotes on reddit

Schnitzelboi · 1 year ago

Fantastic! Genuinely looking for excuses to try this now. Thank you!

2 upvotes on reddit

EmergeHolographic · 1 year ago

Love to see it! There's something incredibly tangible about observing data this way. Stereoscopy is so cool for this; I make some data visualizations too, I've also uploaded stereo footage of the sun. ::)

I was heavily inspired by Magic Eye as well so it's nice to see those books still resonating.

2 upvotes on reddit

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r/dataisbeautiful • [9]

Summarize

What are the most used tools for Data Visualization? I am a newbie. Want to develop info-graphics like these. TIA

Posted by Massive_Shoulder_386 · in r/dataisbeautiful · 3 years ago

reddit.com

227 upvotes on reddit

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PietroViolo · 3 years ago

I made the first graph in R. You can find the code on my Github. I made it pretty in Photoshop, the PSD file is also on Github.

18 upvotes on reddit

HendrixLivesOn · 3 years ago

Excel, Tableau, R, Power BI..

57 upvotes on reddit

niehle · 3 years ago

Python :)

28 upvotes on reddit

ndjdjdjdjsjal · 3 years ago

How do you do this in Python

9 upvotes on reddit

mk100100 · 3 years ago

By far the most used is Excel

4 upvotes on reddit

2-buck · 3 years ago

I like how most of the replies have nothing to do with the question

30 upvotes on reddit

kp_320 · 3 years ago

Right? And the one the answers it is like "Excel" which is the least visual tool I can think of

25 upvotes on reddit

bruceyj · 3 years ago

Lowest barrier to entry making charts, sure, but nothing like the ones shown lol

3 upvotes on reddit

Normal_Juggernaut · 3 years ago

Didn't realise India was that close to China in population size.

7 upvotes on reddit

rj_1024 · 3 years ago

We are in it to win it. 🏆

1 upvotes on reddit

N00bHunter69 · 3 years ago

According to UN reports, India is going to surpass china in 2023

18 upvotes on reddit

Jdam1138 · 3 years ago

That's a lovely looking futbol

9 upvotes on reddit

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r/ProductManagement • [10]

Summarize

How do you analyze data at your job? How often do you use SQL?

Posted by Furiosa_T · in r/ProductManagement · 6 days ago

I'm curious how people interact with data in their daily PM lives, how often do you use SQL? What do you use data for: feature prioritization? user behavior and engagement? product health?

I work on a low-volume data B2B software, and there's more of a qualitative data gathering/analysis than quantitative. I'd like to find a new job and anticipate having data-rich product. Curious what it is like?

27 upvotes on reddit

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minimon23 · 6 days ago

I've noticed a few Product Manager job descriptions mentioning SQL skills as a requirement. I've never used it routinely as I've always been able to get the data I need from an engineer or data scientist in the team. I've learnt a bit of SQL in the past as I used to design SQL tools for developers. But as a PM I'm now revisiting and doing a basic SQL course on Code Academy.

12 upvotes on reddit

ant3k · 6 days ago

You’ve hit on a point which probably job descriptions are looking for. I’d use SQL for cases where I wanted an ad hoc answer without burdening others with the work.

My queries were never that advanced, and it’s easier with AI to help, but some basic skills can go far to enable self sufficiency.

I’d then only bother the Data Engineering lead after attempting and showing my attempt at which point they were always very willing to use their superior skills to help.

But yeah, it’s also feasible you can get the answers in your org without SQL, yet some companies will value it. It’s probably a mix of self sufficiency for some companies and others expecting more advanced skills beyond the use cases I’ve mostly used it for.

8 upvotes on reddit

perplex1 · 5 days ago

Do you find that relying on technical experts slows down delivery cycles for your product? Does your technical experts get burdened with everyone going to them and queuing your request, and you always have to follow up with them to be like, hey any updates? and they are like, no not yet? No? Just me? Okay

1 upvotes on reddit

minimon23 · 5 days ago

I can see that being a problem which is why I want to be more self-reliant. In my previous two organisations the engineers and data scientist I worked with were very quick to retrieve the data I asked for and didn't need any chasing at all. One of the team was a database developer and wasn't always part of the sprint cycles as there was quite a lot of maintenance to do, fixing pipelines etc.

1 upvotes on reddit

thepixelpenguinn · 6 days ago

I don’t use SQL really ever at all. I typically use Google Sheets to analyze data!

29 upvotes on reddit

mister-noggin · 6 days ago

I only use SQL on personal stuff. It's been a long time since I've been given access to a production database.

8 upvotes on reddit

musculard · 6 days ago

If the devs don't give us a dump, we riot! (Or make them do the pulls)

4 upvotes on reddit

Independent_Pitch598 · 6 days ago

SQL is a bread and butter.

For any metric: revenue, performance, etc…

28 upvotes on reddit

EmotionSlow1666 · 6 days ago

I spend 20% of my bandwidth in pulling & analyzing data. I use SQL & python + AI (Gemini). Having the right data handy always gave me an edge in stakeholder alignment.

As a PM ideally I should get it from my data analyst, but my DAs are often pulled for “urgent” leadership asks. DIY is better than waiting for someone to give me the data.

27 upvotes on reddit

Furiosa_T · OP · 6 days ago

What kind of product do you work on? And what kind of analysis do you do?

1 upvotes on reddit

EmotionSlow1666 · 6 days ago

E-commerce product, I mainly do analysis on the transactional data

2 upvotes on reddit

Snoo91508 · 6 days ago

A bit, I use Claude code to write the SQL for me but know enough to correct it if it’s wrong

6 upvotes on reddit

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