Python for Data Analysts

Python list methods

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Python Lambda Function

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10 Ways to Speed Up Your Python Code 1. List Comprehensions numbers = [x**2 for x in range(100000) if x % 2 == 0] instead of numbers = [] for x in range(100000): if x % 2 == 0: numbers.append(x**2) 2. Use the Built-In Functions Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution. 3. Function Calls Are Expensive Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration. 4. Lazy Module Importing If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything. 5. Take Advantage of Numpy Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter. 6. Try Multiprocessing Multiprocessing can bring large performance increases to a Python script, but it can be difficult to implement properly compared to other methods mentioned in this post. 7. Be Careful with Bulky Libraries One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code. 8. Avoid Global Variables Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible. 9. Try Multiple Solutions Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure. 10. Think About Your Data Structures Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets. Best Programming Resources: https://topmate.io/coding/898340 All the best 👍👍

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Pandas is a powerful and versatile library in Python, especially for data science tasks. Here are some key Pandas methods that are widely used: Data Loading and Creation * read_csv(): Reads data from a CSV file into a DataFrame. * read_excel(): Reads data from an Excel file into a DataFrame. * DataFrame(): Creates a new DataFrame from a dictionary, list, or NumPy array. Data Exploration and Selection * head(): Returns the first few rows of a DataFrame. * tail(): Returns the last few rows of a DataFrame. * shape(): Returns the dimensions of a DataFrame (rows, columns). * info(): Provides summary information about the DataFrame, including data types and missing values. * describe(): Generates summary statistics for numerical columns. * loc[]: Selects rows and columns by label. * iloc[]: Selects rows and columns by integer position. * filter(): Selects columns by name. Data Cleaning and Transformation * dropna(): Removes rows or columns with missing values. * fillna(): Fills missing values with a specified value or strategy. * drop_duplicates(): Removes duplicate rows. * apply(): Applies a function to each element or row/column. * groupby(): Groups data based on one or more columns and performs aggregate functions. * pivot_table(): Creates a pivot table for data summarization. * merge(): Merges DataFrames based on a common column. Data Visualization * plot(): Creates various types of plots (line, bar, scatter, etc.). * hist(): Creates a histogram. * boxplot(): Creates a box plot. These are just a few examples of the many powerful methods that Pandas offers. By mastering these methods, you can efficiently load, clean, transform, analyze, and visualize data for your data science projects. Example: import pandas as pd # Load data from a CSV file df = pd.read_csv('data.csv') # Select the first 5 rows print(df.head()) # Group data by a column and calculate the mean grouped_df = df.groupby('column_name').mean() # Create a bar plot grouped_df.plot(kind='bar')

Starting your career with Python is an excellent choice due to its versatility and broad range of applications. As you advance, you might discover various specializations that align with your interests: • Data Science: If you’re excited about analyzing data and extracting insights, diving deeper into data science might be your next step. You’ll use Python libraries like Pandas, NumPy, and SciPy to work with data and build predictive models. • Machine Learning: If you’re fascinated by building intelligent systems that learn from data, specializing in machine learning could be your calling. Python frameworks like TensorFlow, Keras, and scikit-learn will be key tools in your toolkit. • Web Development: If you enjoy creating web applications, focusing on web development with Python could be a great path. Frameworks like Django and Flask allow you to build robust and scalable web solutions. • Automation and Scripting: If you’re interested in automating repetitive tasks and creating scripts to improve efficiency, Python is a perfect choice. You'll use libraries like Selenium and BeautifulSoup for web scraping, and automation tools like Celery for task scheduling. • Data Engineering: If you’re keen on building data pipelines and managing large datasets, specializing in data engineering might be your next move. Python’s integration with tools like Apache Airflow and Apache Spark can be particularly useful. • DevOps: If you enjoy managing and automating the deployment of applications, focusing on DevOps with Python might be a good fit. Python can be used for scripting and integrating with tools like Docker and Kubernetes. • Game Development: If you're interested in creating games, you might explore game development with Python using libraries like Pygame, which can be a fun and creative way to apply your programming skills. Even if you stick with general Python programming, there’s always something new to explore, especially with the constant evolution of libraries and tools. The key is to continue coding, experimenting with different projects, and staying updated with industry trends. Each step in Python opens up new opportunities to build diverse and impactful applications. I have curated the best interview resources to crack Python Interviews 👇👇 https://topmate.io/analyst/907371 Hope you'll like it Like this post if you need more resources like this 👍❤️

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Here's a list of important Pandas functions along with brief descriptions: pd.read_csv() – Reads a CSV file into a DataFrame. pd.DataFrame() – Creates a DataFrame from various input formats (e.g., lists, dictionaries). df.head() – Displays the first few rows of the DataFrame. df.tail() – Displays the last few rows of the DataFrame. df.info() – Provides a concise summary of the DataFrame (data types, non-null counts). df.describe() – Provides descriptive statistics for numerical columns. df.columns – Returns the column labels of the DataFrame. df.index – Returns the index (row labels) of the DataFrame. df.shape – Returns the dimensions of the DataFrame (rows, columns). df.dtypes – Returns the data types of each column. df.isnull() – Detects missing values (returns Boolean values). df.fillna() – Fills missing values with a specified value. df.dropna() – Removes missing values from the DataFrame. df.drop() – Drops specified labels from rows or columns. df.duplicated() – Returns Boolean Series denoting duplicate rows. df.drop_duplicates() – Removes duplicate rows from the DataFrame. df.sort_values() – Sorts the DataFrame by the values of one or more columns. df.groupby() – Groups data by one or more columns for aggregation. df.apply() – Applies a function along an axis of the DataFrame. df.loc[] – Accesses a group of rows and columns by labels or Boolean arrays. df.iloc[] – Accesses rows and columns by index position. df.merge() – Merges two DataFrames on common columns or indices. df.join() – Joins two DataFrames based on their index. df.concat() – Concatenates multiple DataFrames along a particular axis. df.pivot_table() – Creates a pivot table for summarizing data. df.melt() – Unpivots the DataFrame from wide to long format. df.rename() – Renames columns or index labels of the DataFrame. df.set_index() – Sets a column as the index of the DataFrame. df.reset_index() – Resets the index to a default integer index. pd.to_datetime() – Converts a column or series to datetime format. pd.cut() – Bins continuous data into discrete intervals. df.value_counts() – Returns a Series of counts for unique values in a column. df.corr() – Computes the pairwise correlation between columns. df.to_csv() – Writes the DataFrame to a CSV file. df.plot() – Creates basic plots from DataFrame data using Matplotlib. These functions cover essential operations in data handling, cleaning, analysis, and visualization using Pandas. I have curated the best interview resources to crack Python Interviews 👇👇 https://topmate.io/analyst/907371 Hope you'll like it Like this post if you need more resources like this 👍❤️

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Planning for Data Science or Data Engineering Interview. Focus on SQL & Python first. Here are some important questions which you should know. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1- Find out nth Order/Salary from the tables. 2- Find the no of output records in each join from given Table 1 & Table 2 3- YOY,MOM Growth related questions. 4- Find out Employee ,Manager Hierarchy (Self join related question) or Employees who are earning more than managers. 5- RANK,DENSERANK related questions 6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.) 7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN. 8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers. 9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure. 10-Identify and remove duplicate records from a table. SQL Interview Resources: https://topmate.io/analyst/864764 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1- Reversing a String using an Extended Slicing techniques. 2- Count Vowels from Given words . 3- Find the highest occurrences of each word from string and sort them in order. 4- Remove Duplicates from List. 5-Sort a List without using Sort keyword. 6-Find the pair of numbers in this list whose sum is n no. 7-Find the max and min no in the list without using inbuilt functions. 8-Calculate the Intersection of Two Lists without using Built-in Functions 9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response. 10-Implement a function to fetch data from a database table, perform data manipulation, and update the database. Python Interview Resources: https://topmate.io/analyst/907371 Join for more: https://t.me/datasciencefun ENJOY LEARNING 👍👍

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Python Interview Questions for data analyst interview Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest. Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.) Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously. Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year. Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other. Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average I have curated the best interview resources to crack Python Interviews 👇👇 https://topmate.io/analyst/907371 Hope you'll like it Like this post if you need more resources like this 👍❤️ #Python

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