Python Data Science Handbook

Python Data Science Handbook: full text in Jupyter Notebooks. This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks.

Creator: Jake Vanderplas
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Fork: 17.1K
Repo: https://github.com/jakevdp/PythonDataScienceHandbook

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Python for Everybody
Charles R. Severance, 2023

Learning Professional Python✅

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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

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Python Most Important Interview Questions

Question 1: Calculate the average stock price for Company X over the last 6 months.

Question 2: Identify the month with the highest total sales for Company Y using their monthly sales data.

Question 3: Find the maximum and minimum stock price for Company Z on any given day in the last year.

Question 4: Create a column in the DataFrame showing the percentage change in stock price from the previous day for Company X.

Question 5: Determine the number of days when the stock price of Company Y was above its 30-day moving average. Question

6: Compare the average stock price of Companies X and Z in the first quarter of the year.

#Data#
----------------------------------------------
import pandas as pd
data = {   'Date': pd.date_range(start='2023-01-01', periods=180, freq='D'),   'CompanyX_StockPrice': pd.np.random.randint(50, 150, 180),   'CompanyY_Sales': pd.np.random.randint(20000, 50000, 180),   'CompanyZ_StockPrice': pd.np.random.randint(70, 200, 180) }

df = pd.DataFrame(data)

Complete Python Course with Building 10 Real-world Applications
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Python for Data Science Roadmap
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Head First Python
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