Python for Data Analysts
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Find top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics
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Python for Data Analysts (@pythonanalyst) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 51 853 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 2 495-o'rinni va Hindiston mintaqasida 6 790-o'rinni egallagan.
š Auditoriya koārsatkichlari va dinamika
Š½ŠµŠ²ŃŠ“Š¾Š¼Š¾ sanasidan buyon loyiha tez oāsib, 51 853 obunachiga ega boāldi.
30 Avgust, 2026 dagi oxirgi maālumotlarga koāra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 94 ga, soānggi 24 soatda esa 10 ga oāzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya oārtacha 3.85% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.96% ini tashkil etuvchi reaksiyalarni toāplaydi.
- Post qamrovi: Har bir post oārtacha 1 994 marta koāriladi; birinchi sutkada odatda 499 ta koārish yigāiladi.
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- Tematik yoānalishlar: Kontent visualization, panda, analyst, sql, analytic kabi asosiy mavzularga jamlangan.
š Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taāriflaydi:
āFind top Python resources from global universities, cool projects, and learning materials for data analytics.
For promotions: @coderfun
Useful links: heylink.me/DataAnalyticsā
Yuqori yangilanish chastotasi (oxirgi maālumot 31 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boālib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taāsir nuqtasiga aylantirishini koārsatadi.
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Obunachilar
+1024 soatlar
+77 kunlar
+9430 kunlar
Postlar arxiv
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š° Learning Python for Data Analysis and Visualization
ā± 21 Hours š¦ 110 Lessons
Learn python and how to use it to analyze,visualize and present data. Includes tons of sample code and hours of video!
Taught By: Jose Portilla
Download Full Course: https://t.me/pythonanalyst/26
Download All Courses: https://t.me/DataAnalystInterview
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Python Generators
In Python, a generator is a function that returns an iterator that produces a sequence of values when iterated over.
Generators are useful when we want to produce a large sequence of values, but we don't want to store all of them in memory at once.
There is a lot of complexity in creating iteration in Python; we need to implement iter() and next() method to keep track of internal states.
It is a lengthy process to create iterators. That's why the generator plays an essential role in simplifying this process. If there is no value found in iteration, it raises StopIteration exception.
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Output of this code?
x =Ā lambdaĀ a, b, c : a + b + c
print(x(5,Ā 6,Ā 2))
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What will be the output of this code in python?
x =Ā lambdaĀ a, b : a * b
print(x(5,Ā 6))
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Example:
Add 10 to argument a, and return the result:
x = lambda a : a + 10
print(x(5))
Output will be 1551 853
Python Lambda:
A lambda function is a small anonymous function.
A lambda function can take any number of arguments, but can only have one expression.
lambda arguments : expression
The expression is executed and the result is returned.
Use lambda functions when an anonymous function is required for a short period of time.
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Syntax:
newlist = [expression for item in iterable if condition == True]
The return value is a new list, leaving the old list unchanged.
The condition is like a filter that only accepts the items that valuate to True. The condition is optional and can be omitted.
The iterable can be any iterable object, like a list, tuple, set etc.
The expression is the current item in the iteration, but it is also the outcome, which you can manipulate before it ends up like a list item in the new list.
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š List comprehensions: List comprehension offers a shorter syntax when you want to create a new list based on the values of an existing list.
Example:
Based on a list of fruits, you want a new list, containing only the fruits with the letter "a" in the name.
Without list comprehension you will have to write a for statement with a conditional test inside:
fruits = ["apple", "banana", "cherry", "kiwi", "mango"]
newlist = []
for x in fruits:
if "a" in x:
newlist.append(x)
print(newlist)
With list comprehension you can do all that with only one line of code:
fruits = ["apple", "banana", "cherry", "kiwi", "mango"]
newlist = [x for x in fruits if "a" in x]
print(newlist)51 853
This cheat sheet includes basic python required for data analysis excluding pandas, numpy & other libraries
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What is Python Loop?
When you want some statements to execute a hundred times, you donāt repeat them 100 times.
Think of when you want to print numbers 1 to 99. Or that you want to say Hello to 99 friends.
In such a case, you can use loops in python.
Here, we will discuss 4 types of Python Loop:
Python For Loop
Python While Loop
Python Loop Control Statements
Nested For Loop in Python
Python While Loop
A while loop in python iterates till its condition becomes False. In other words, it executes the statements under itself while the condition it takes is True.
Python For Loop
Python for loop can iterate over a sequence of items. The structure of a for loop in Python is different than that in C++ or Java.
That is, for(int i=0;i<n;i++) wonāt work here. In Python, we use the āinā keyword.
Nested for Loops in Python
You can also nest a loop inside another. You can put a for loop inside a while, or a while inside a for, or a for inside a for, or a while inside a while.
Or you can put a loop inside a loop inside a loop. You can go as far as you want.
Loop Control Statements in Python
Sometimes, you may want to break out of normal execution in a loop.
For this, we have three keywords in Python- break, continue, and Python
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š What is Python Data Structures?
You can think of a data structure as a way of organizing and storing data such that we can access and modify it efficiently.
We have primitive data types like integers, floats, Booleans, and strings.
š What is Python List?
A list in Python is a heterogeneous container for items. This would remind you of an array in C++, but since Python does not support arrays, we have Python Lists.
š Python Tuple
This Python Data Structure is like a, like a list in Python, is a heterogeneous container for items.
But the major difference between the two (tuple and list) is that a list is mutable, but a tuple is immutable.
This means that while you can reassign or delete an entire tuple, you cannot do the same to a single item or a slice.
š Python Dictionaries
Finally, we will take a look at Python dictionaries. Think of a real-life dictionary. What is it used for? It holds word-meaning pairs. Likewise, a Python dictionary holds key-value pairs. However, you may not use an unhashable item as a key.
To declare a Python dictionary, we use curly braces. But since it has key-value pairs instead of single values, this differentiates a dictionary from a set.
