Python for Data Analysts

𝗚𝗲𝘁 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 𝗜𝗻 𝗔𝗺𝗮𝘇𝗼𝗻, 𝗚𝗼𝗼𝗴𝗹𝗲, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗡𝗩𝗜𝗗𝗜𝗔, 𝗮𝗻𝗱 𝗠𝗲𝘁𝗮 (𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸) 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲𝘀𝗲 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀😍 1️⃣ Amazon Interviewing Guide 2️⃣ Google Interview Tips 3️⃣ Microsoft Hiring Tips 4️⃣ NVIDIA Hiring Process 5️⃣ Meta Onsite SWE Prep Guide 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/40OSJJ6 Crack Interview & Get Your Dream Job In Top MNCs

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How to be a Prompt Engineer 101 The shortest and most comprehensive guide 1. start with an explanation Make a description and character situation at the beginning of the Prompt Error example: Please help me read the following code: {your input here} Correct example: Now let's play the role, you are a senior information security engineer, I will give you a piece of code, please help me read the code and point out where there may be security vulnerable. Text: """ {your input here} """ 2. Prompt to describe the situation In the prompt, it is necessary to describe the context, result, length, format and style as much as possible Error example: Write a short story for kids Correct example: Write a funny soccer story for kids that teaches the kid that persistence is the key for success in the style of Rowling. 3. gives output in the format If you are doing data analysis, please give the input template of the format Error example: Extract house pricing data from the following text. Text: """ {your text containing pricing data} """ Correct example: Extract house pricing data from the following text. Desired format: """ House 1 | $1,000,000 | 100 sqm House 2 | $500,000 | 90 sqm ... (and so on) """ Text: """ {your text containing pricing data} """ 4. Add some example questions and answers Sometimes adding some question and answer examples can make GPT more intelligent Correct example: Extract brand names from the texts below. Text 1: Finxter and YouTube are tech companies. Google is too. Brand names 2: Finxter, YouTube, Google ### Text 2: If you like tech, you'll love Finxter! Brand names 2: Finxter ### Text 3: {your text here} Brand names 3: The question and answer example is also a standard template example in fine-tune 5. Simplify the sentence and clarify the purpose Keep your words as short as possible and don't say useless content Error example: ChatGPT, write a sales page for my company selling sand in the desert, please write only a few sentences, nothing long and complex Correct example: Write a 5-sentence sales page, sell sand in the desert. 6. Good at using introductory words Error example: Write a Python function that plots my net worth over 10 years for different inputs on the initial investment and a given ROI Correct example: # Python function that plots net worth over 10 # years for different inputs on the initial # investment and a given ROI import matplotlib def plot_net_worth(initial, roi):

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👉 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)

Frequently asked Python practice questions and answers in Data Analyst Interview: 1.Temperature Conversion: Write a program that converts a given temperature from Celsius to Fahrenheit or from Fahrenheit to Celsius based on user input. temp = float(input('Enter the temperature: ')) unit = input('Enter the unit (C/F): ').upper() if unit == 'C': converted = (temp * 9/5) + 32 print(f'Temperature in Fahrenheit: {converted}') elif unit == 'F': converted = (temp - 32) * 5/9 print(f'Temperature in Celsius: {converted}') else: print('Invalid unit') 2.Multiplication Table: Write a program that prints the multiplication table of a given number using a while loop. num = int(input('Enter a number: ')) i = 1 while i <= 10: print(f'{num} x {i} = {num * i}') i += 1 3.Greatest of Three Numbers: Write a program that takes three numbers as input and prints the greatest of the three. num1 = float(input('Enter first number: ')) num2 = float(input('Enter second number: ')) num3 = float(input('Enter third number: ')) if num1 >= num2 and num1 >= num3: print(f'The greatest number is {num1}') elif num2 >= num1 and num2 >= num3: print(f'The greatest number is {num2}') else: print(f'The greatest number is {num3}') 4.Sum of Even Numbers: Write a program that calculates the sum of all even numbers between 1 and a given number using a while loop. num = int(input('Enter a number: ')) total = 0 i = 2 while i <= num: total += i i += 2 print(f'The sum of even numbers up to {num} is {total}') 5.Check Armstrong Number: Write a program that checks if a given number is an Armstrong number. num = int(input('Enter a number: ')) sum_of_digits = 0 original_num = num while num > 0: digit = num % 10 sum_of_digits += digit ** 3 num //= 10 if sum_of_digits == original_num: print(f'{original_num} is an Armstrong number') else: print(f'{original_num} is not an Armstrong number') 6.Reverse a Number: Write a program that reverses the digits of a given number using a while loop. num = int(input('Enter a number: ')) reversed_num = 0 while num > 0: digit = num % 10 reversed_num = reversed_num * 10 + digit num //= 10 print(f'The reversed number is {reversed_num}') 7.Count Vowels and Consonants: Write a program that counts the number of vowels and consonants in a given string. string = input('Enter a string: ').lower() vowels = 'aeiou' vowel_count = 0 consonant_count = 0 for char in string: if char.isalpha(): if char in vowels: vowel_count += 1 else: consonant_count += 1 print(f'Number of vowels: {vowel_count}') print(f'Number of consonants: {consonant_count}') Python Interview Q&A: https://topmate.io/coding/898340 Like for more ❤️ ENJOY LEARNING 👍👍

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Data Cleaning Techniques in Python ✅

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Python Roadmap 🔝💪

Pandas Functions

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5 Pandas Functions to Handle Missing Data 🔹 fillna() – Fill missing values with a specific value or method 🔹 interpolate() – Fill NaNs with interpolated values (e.g., linear, time-based) 🔹 ffill() – Forward-fill missing values with the previous valid entry 🔹 bfill() – Backward-fill missing values with the next valid entry 🔹 dropna() – Remove rows or columns with missing values #Pandas

𝐒𝐢𝐦𝐩𝐥𝐞 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 😃 🙄 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠? Imagine you're teaching a child to recognize fruits. You show them an apple, tell them it’s an apple, and next time they know it. That’s what Machine Learning does! But instead of a child, it’s a computer, and instead of fruits, it learns from data. Machine Learning is about teaching computers to learn from past data so they can make smart decisions or predictions on their own, improving over time without needing new instructions. 🤔 𝐖𝐡𝐲 𝐢𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬? Machine Learning makes data analytics super powerful. Instead of just looking at past data, it can help predict future trends, find patterns we didn’t notice, and make decisions that help businesses grow! 😮 𝐇𝐨𝐰 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬? ✅ 𝐋𝐞𝐚𝐫𝐧 𝐏𝐲𝐭𝐡𝐨𝐧: Python is the most commonly used language in ML. Start by getting comfortable with basic Python, then move on to ML-specific libraries like: 𝐩𝐚𝐧𝐝𝐚𝐬: For data manipulation. 𝐍𝐮𝐦𝐏𝐲: For numerical calculations. 𝐬𝐜𝐢𝐤𝐢𝐭-𝐥𝐞𝐚𝐫𝐧: For implementing basic ML algorithms. ✅ 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬 𝐨𝐟 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: ML relies heavily on concepts like probability, distributions, and hypothesis testing. Understanding basic statistics will help you grasp how models work. ✅ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐨𝐧 𝐑𝐞𝐚𝐥 𝐃𝐚𝐭𝐚𝐬𝐞𝐭𝐬: Platforms like Kaggle offer datasets and ML competitions. Start by analyzing small datasets to understand how machine learning models make predictions. ✅ 𝐋𝐞𝐚𝐫𝐧 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Use tools like Matplotlib or Seaborn to visualize data. This will help you understand patterns in the data and how machine learning models interpret them. ✅ 𝐖𝐨𝐫𝐤 𝐨𝐧 𝐒𝐢𝐦𝐩𝐥𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Start with basic ML projects such as: -Predicting house prices. -Classifying emails as spam or not spam. -Clustering customers based on their purchasing habits. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

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