Pandas offers rich functionalities for aggregating data and doing actions on these collections. This can entail dividing the data into groups, individually applying a function to each group, and then aggregating the outcomes.
Example: Grouping data and performing aggregation.
import pandas as pd
# Sample data
data = {
"City": ["New York", "Los Angeles", "New York", "Los Angeles", "New York", "Los Angeles"],
"Year": [2010, 2010, 2011, 2011, 2012, 2012],
"Population": [8175133, 3792621, 8274932, 3819702, 8346693, 3857799]
}
df = pd.DataFrame(data)
# Grouping by city and calculating average population
grouped = df.groupby("City")
avg_population = grouped["Population"].mean()
print(avg_population)
The average population of each city is computed in this example after the DataFrame df is grouped by City. This demonstrates how data can be readily aggregated according to groupings.
Pandas offers rich functionalities for aggregating data and doing actions on these collections. This can entail dividing the data into groups, individually applying a function to each group, and then aggregating the outcomes.
Example: Grouping data and performing aggregation.
pythonimport pandas as pd # Sample data data = { "City": ["New York", "Los Angeles", "New York", "Los Angeles", "New York", "Los Angeles"], "Year": [2010, 2010, 2011, 2011, 2012, 2012], "Population": [8175133, 3792621, 8274932, 3819702, 8346693, 3857799] } df = pd.DataFrame(data) # Grouping by city and calculating average population grouped = df.groupby("City") avg_population = grouped["Population"].mean() print(avg_population)
The average population of each city is computed in this example after the DataFrame df is grouped by City. This demonstrates how data can be readily aggregated according to groupings.
Python is commonly used for developing websites and software, task automation, data analysis, and data visualisation. Since it's relatively easy to learn, Python has been adopted by many non-programmers, such as accountants and scientists, for a variety of everyday tasks, like organising finances.
Learning Curve: Python is generally considered easier to learn for beginners due to its simplicity, while Java is more complex but provides a deeper understanding of how programming works.
The point is that Java is more complicated to learn than Python. It doesn't matter the order. You will have to do some things in Java that you don't in Python. The general programming skills you learn from using either language will transfer to another.
Read on for tips on how to maximize your learning. In general, it takes around two to six months to learn the fundamentals of Python. But you can learn enough to write your first short program in a matter of minutes. Developing mastery of Python's vast array of libraries can take months or years.
6 Top Tips for Learning Python
The following is a step-by-step guide for beginners interested in learning Python using Windows.
Best YouTube Channels to Learn Python
Write your first Python programStart by writing a simple Python program, such as a classic "Hello, World!" script. This process will help you understand the syntax and structure of Python code.
The average salary for Python Developer is ₹5,55,000 per year in the India. The average additional cash compensation for a Python Developer is within a range from ₹3,000 - ₹1,20,000.
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