Matplotlib Tutorial (Part 2): Bar Charts and Analyzing Data from CSVs
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- Опубліковано 14 лип 2024
- In this video, we will be learning how to create bar charts in Matplotlib.
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In this Python Programming video, we will be learning how to create bar charts in Matplotlib. Bar charts are great for visualizing your data in a way where you can clearly see the total values for each category. We'll learn how to create basic bar charts, bar charts with side-by-side bars, and also horizontal bar charts. We will also learn how to load our data from a CSV file instead of having it directly in our script. Let's get started...
The code from this video (with added logging) can be found at:
bit.ly/Matplotlib-02
CSV Tutorial - • Python Tutorial: CSV M...
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#Python #Matplotlib
Who needs python docs when you have such an amazing teacher
True:
Exactly Brother
Where is the CSV for this? I don't see it in the description. Thank you!
True
the teacher
I hope everyone finds this video helpful. The next video of the series will be posted tomorrow at the same time. The next video will cover how to create pie charts.
I'd like to thank Brilliant for sponsoring this series. If you'd like to check them out then you can sign up with this link and get 20% off your premium subscription:
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As usual lovely!!!!!!!
It's a great tutorial; the only thing I was missing is to add total values on the top of each bar charts (can be trickier for stacked bar chart)
Thank you, sir, for providing top-class tutorials for free.
Hello Corey!
Please can you advise:
1. how did you the clean the data within the column " LanguageWorkedWith" so that you can generate this clear data?
2. After I have split it and save it to another csv file a part from the main, the is the output: [(" 'JavaScript'", 53020), (" 'HTML/CSS'", 39761), (" 'Java'", 29863), ("['Bash/Shell/PowerShell'", 28340), (" 'SQL']", 28178), (" 'Python'", 26185), (" 'PHP'", 20394), (" 'SQL'", 19094), (" 'TypeScript']", 16091), ("['HTML/CSS'", 15322)]
[Finished in 33.6s]
3. According the below output , how will I do so that it can bring the sum exact of the occurrence of the languages as it look like not doing it?
Thank you,
Where is the CSV for this? I don't see it in the description. Thank you!
In case you don't know, the shortcut for 8:13 in jupyter notebook is *Ctrl + left mouse click* on the different lines one by one. You can write at different lines at the same time.
Nice! Thanks form 2years later!
alt + left mouse in vs code
thanks u
@@jeffery_tang
These series is much better than the curses in Udemy I paid for. Thank you very much.
what "curses"
@@DendrocnideMoroides wannabe savage
@@DendrocnideMoroides I vote we call them curses from now on
No body teaches like you. You are the best. Amazing delivery of information, truly useful tutorials. Thank you so much.
Corey, you are great teacher. You have rare ability to explain calmly. Much appreciating your efforts.
This series with pandas one has taken my skills to a new level.
Excellent tutorial Corey! Real life stuff and practical, including the use of Counter. It's important to show these data preparation steps. Very helpful indeed, thank you.
such a great Python instructor with an angelic voice. Thank you so much 😊
Right from reading data from a csv file to plotting it, you helped a lot of people.
Man, you are awesome, everything I have learned about python started from your channel, I wish you the very best all success, as you make everyone happy, keep up the excellent work, we all heavily rely on you.
Thanks! That's very kind of you.
Amazing content Corey. The way you simplify the material and explain is awesome, many thanks. Can you please also do a video showing your setup and how you make video's. Thanks !!!
I can't express how amazing this video is. What a great teacher you are. 🔥🔥
What I really like is your videos, Corey. I can learn Python and English ;D
Thanks!!
At 8:12, when you selected multiple locations and simultaneously type the same code to multiple lines, my world just expanded!
This is gold! Thank you very much for doing this, you have incredible talent to explain complicated stuff in an easy manner, keep up good work :)))
Another great video, thank-you. A Pandas series of videos would be awesome!
23:40 here's that one liner if anybody's interested. Personally, I like this more.
languages, popularity = map(list, zip(*language_counter.most_common(15)))
Really nice! Could you please explain what the "*" symbol does?
nice
Or just: list(zip(*language_counter.most_common(15))). Map is unnecessary as list() automatically maps over an Iterable
@@jg9193 but if you don't use map(list, iterable) then languages and popularity will be tuples so you cannot use reverve() for the rest of the tutorial. Or languages, popularity = [list(e) for e in zip(*language_counter.most_common(15))] without map
@@corben3348 Fair point, I didn't think of that. That said, he could just do languages[::-1] instead of languages.reverse() to reverse a tuple
Then again, using list() would even be unnecessary if he did that
Thank you man, appreciate the effort and time you've put in creating such amazing content as these.
Another great video form you, Corey. Thank you, you made my day everyday!!
Thank you for your work. I enjoy every lesson.
This is the best Corey; Thank you very much from my 🧠 and ❣
thank you very much, very clear and straight to the point!
Thank you very much bro, Greetings from Azerbaijan.
Really nice work over here, the most important man on youtube for me.
Your videos are just sprinkled with little golden nuggets! I love it ❤
What a perfect lesson, fast and insightful pieces of knowledge...
The great thing about your tutorials is that despite main topic, you learn a lot useful tricks, modules etc.
Corey Schafer saves my life once again...
Deep gratitude for your work, man!
Thank you for sharing your knowledge!
This is the best content on UA-cam, thank you for so much
thank you so much sir,really glad i found ur playlist and didn't waste time on other platforms
Your explanation is awesome...thank you so much ...A great teacher for a lifetime...
Very helpful video. The pandas method is much simpler and easier to understand. Thanks Corey!
Thanks a lot Corey. Really your videos are endless treasure.
Just a way for plotting bar charts for more than one dataset on the same plot without need to numpy. Just use built-in map function.
width = 0.25 #Width of bar
plt.bar(list(map(lambda x: x-width/2, age_x)), salaries1, color = 'k', width = width)
plt.bar(list(map(lambda x: x+width/2, age_x)), salaries2, color = 'r', width = width)
I think your videos are more understandable than rest of the youtube channels
This is pure Gold .
Thank you very much.its a great tutorial as always
Great video as always! Really helpful for detailed explanation.
That's true......you are an amazing teacher. This was very helpful
Thank you so much for your hard work! You are a great teacher and your video tutorial represent a valuable resource :)
You explain things really well, kudos!
I just came across this series of videos. They are extremely good :-)
best matplotlib tutorial ever!
Such a great help, thankyou so much!
thank you for always showing the clear code before abbreviating
great tutorial! the best!! thanks for teaching us!
You're making machine learning interesting, thank you
for those wondering how to obtain the CSV file, once you've clicked on it and you see all of the data in your web browser, just right click and say save as
jaja that was very useful, Thanks!
Thanks so much!
Great explanation...thanks a lot Corey sir
Great videos. I'm so grateful...
I can't believe we need this hack to make a bar chart.
Great video.
As you mentioned Zip can also be used
language = cnt.most_common(10)
language.reverse()
language_X, language_Y = list(zip(*language))
plt.barh(language_X, language_Y)
Very informative video, good job Mr Corey
Great video! Thank you man
This is the best fantastic lecture for the relation of Python and Pandas I've ever seen!!!!!!!!!!!!!!
Xie Xie!!!
Amazing Tutorials Thanks soo much !
Another great video. Thanks!!
Thank you for the series of video! :)
thank you professor. love from india. u know what i dont like to read those documentation. when i saw your videos.
thank you!!!! you ar an excellent teacher
2 weeks later and still not a single dislike on this video
you are amazing, waiting for your data science ( ML, AI ) course...... THANKS A LOT!
Thank you so much.. It's a great vedio....
Programming is so fun.
Corey. Million thanks bro
thank you Brilliant for supporting Corey
Very nice your explanations. Congratulations.
Thanks for this. Great lesson. As you say, creating multiple bars seems extraordinarily hacky. I would have thought this would be easily dealt with by a plotting library
Amazing video !
Great video!
you are a life saviour for people like me
great tutorial, thanks
ty soo much .. yu are the best ..
Thank you very much. Please, please come back!
Best of the best!
Great Matplotlib tutorial. But I feel like this is where Pandas also really comes to play, we can use sep = ; inside of the read_csv function instead of creating a custom function. Also, using iloc and loc for indexes and many more awesome built in functions
Counter() is the best thing I learned today
@Corey Schafer .. I came up with below function which will handle the bar widths for multiple bar plots by itself. Just in case anybody wants to use it :
ages_x = np.asarray([25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35])
count = 5
width = 0.8/count
def width_cal(position):
shift = np.array([])
if count < 2:
return ages_x
if count % 2 == 0:
for i in range(1, count, 2):
shift = np.append(shift, (width/2 * i))
shift = np.sort(np.append(shift, np.negative(shift)))
else:
for i in range(0, count, 2):
shift = np.append(shift, (width/2 * i))
shift = np.unique(np.sort(np.append(shift, np.negative(shift))))
shift = np.around(shift, decimals=3)
return ages_x + shift[position]
plt.bar(width_cal(0), dev_y, width=width, color='#444444', label="All Devs")
great instructor
Great, amazing video
hi Corey....god bless you
Great tutorial sir
Thanks you and Brilliant
You can also do this for geting the languages and popularity lists.
languages = list(map(lambda x: x[0], language_counter.most_common(15)))
print(languages)
popularity = list(map(lambda x: x[1], language_counter.most_common(15)))
print(popularity)
thank you for python tutorial
Thank you lot sir 😃
These videos are great! Coming from R (and ggplot) I was a tad skeptical that Python could emulate R when it came to data viz, but I stand corrected.
You're right
Tq sir
This is for u sir
while 2 < 3:
print('thank you soo much')
The best in you tube .👏
Thanks man!!
Please do a tutorial on numpy as well, it would be super helpful, by the way awesome content😁
we can also use the dictionary's keys() and values() for getting x and y axis. x_axis = list(dict.keys())
Thanks!
could you do more videos about data cleaning? Thank you and I love your blog.
Wonderful!
Thanks a lot CMS
collins anele That probably won’t work because value_counts() won’t split the data at the semicolons, so “Python;JavaScript” would be one value instead of two.
You are right. I just realised that.
I love Corey's videos*(infinite).
Hi Corey, thank you for the wonderful session , I have stuck at this point with the last example :-import csv
import numpy as np
import pandas as pd
from collections import Counter
from matplotlib import pyplot as plt
plt.style.use("fivethirtyeight")
data = pd.read_csv('data.csv')
ids = data['Responder_id']
lang_responses = data['LanguagesWorkedWith']
language_counter = Counter()
for response in lang_responses:
language_counter.update(response.split(';'))
languages = []
popularity = []
for item in language_counter.most_common(15):
languages.append(item[0])
popularity.append(item[1])
languages.reverse()
popularity.reverse()
plt.barh(languages, popularity)
plt.title("Most Popular Languages")
# plt.ylabel("Programming Languages")
plt.xlabel("Number of People Who Use")
plt.tight_layout()
plt.show()
### I am getting an error like AttributeError: 'float' object has no attribute 'split' ...Please explain..
Thx a lot for this...
Fantastic video. Exactly the type of content that I was looking for to create beautiful bar graphs.