DEV Community: Jeornee

Measure of center. What is this dataset's typical behavior?

Jeornee — Tue, 21 Jan 2025 22:56:05 +0000

Introduction

Measure of central tendency is important when working with datasets because it gives us a summary of the data, it provides a single value that is a summarization of the center point of the dataset.
This value gives insight into the dataset as a whole and allows for easier comparison, understanding and decision-making.

Measure of center

Mean(Arithmetic Average)

Mean it the addition/sum of all individual values and divided by the total number

In the case of the scores of 12 students

Median(Middle Value)
Median is the middle value. 50% above and 50% below after being sorted.

Mode(Most Frequent Value)
Mode is the most frequently occurring value in a dataset.

Mean is sensitive to extra values, so, they work well with symmetrical charts, right or left skewed charts wouldn't work well with mean.
However, median would be a better option.

What is spreed?

Variance

Variance is the average distance from each data point from the mean.

Standard deviation

Standard deviation is the square root of the variance.
Better than variance because you can easily wrap your head around as it is the square root of the variance.

Interquartile Range (IQR)
Measures the spread of the middle 50% of data by subtracting the first quartile (Q1) from the third quartile (Q3).

(Q1) indicates the boundary for the lower quarter of the dataset, showing where the first 25% of values lie when the data is sorted in ascending order.

(Q3) is the value below which 75% of the data falls. It represents the 75th percentile of the dataset.
It marks the upper boundary of the third quarter of the data, leaving only the top 25% of values above it.

Range
The simplest measure of spread, calculated as the difference between the maximum and minimum values in a dataset.

Introduction to data analysis with Python: Part 2 - Lists, Tuples and Dictionaries

Jeornee — Mon, 20 Jan 2025 22:59:11 +0000

Introduction

For everything in life, there are building blocks, and to fully understand a concept, we usually have to go down to those basics.
Python has building blocks that we’re going to dive into in this article.

Lists

A list is an ordered, mutable collection of items. They can hold different data types.

Characteristics of Lists

They are ordered.
They can be changed (mutable).
They can hold different data types.
They can be indexed.
They are stored in square brackets [ ].
They accept duplicate elements.

Examples of lists

Indexing
Items in a list can be accessed using a method called indexing.
Indexes, in this context, are numbers assigned to the items in a list. The items are indexed/numbered starting from 0.

An example would be:

To fetch items, we write:

Positive indexing: This is simply calling items with their natural index numbers.

Negative indexing: Negative indexing works backward. If you want to pick the -4 item, you count from right to left starting from 1.
This could be useful when you have a long list and want to access the second-to-last item. For instance, writing -2 would do that.

Slicing indexing: Slicing allows us to pick a group of items. When slicing, you need two indexes—the index to start slicing from and the index to end at. However, the ending index is not included in the result.
As we see in the example below, print(a_list[1:4]) prints items at index 1 to 3. Index 4 (Tolu) is excluded.
We can also slice like this: print(a_list[:3]). This asks Python to return the values from the beginning up to index 2, as index 3 is excluded.

Skipping indexing: As the name implies, skipping indexing allows us to skip items we do not want. In a list of numbers, skip indexing can give us only even numbers.

Mutability of lists
As we’ve mentioned earlier, lists are mutable. We can update them and delete items from them.

Tuples

A tuple is an immutable and ordered collection of elements.

Characteristics of Tuples

They are ordered.
They are unchangeable (immutable).
They can hold different data types.
They can be indexed.
They are stored in curved brackets ().
They accept duplicate elements.
They are faster than lists because they are immutable.

Example of Tuples

Tuples share the same indexing style as Lists.

Immutability of Tuples: They cannot be deleted or updated.

Dictionaries

A dictionary is an unordered, mutable collection of key-value pairs.

Characteristics of Dictionaries

They are unordered.
They can be changed (mutable).
They can hold different data types.
They are indexed not by position but by keys.
They are stored in curly brackets {}.
They accept duplicate value elements.
Keys must be unique.

Example of a dictionary

To retrieve values, we use the keys. In this case, the keys are: name, age, gender, country, friend.

To update, you assign a new value using an equals sign (=).

Introduction to data analysis with Python: Part 1 - Data types and Variables

Jeornee — Sat, 14 Dec 2024 01:15:41 +0000

Data Types

Data types are classifications that specify the kind of value/data a variable can hold.

They include:

Integer or int: Whole numbers (e.g., 1, 43, 78, 100, 34).
String or str: Text data enclosed in quotes. Depending on the programming language, these can be in single quotes ('') or double quotes (""). (e.g., "Grace", "height", "school")
Boolean or bool: Represents truth values: True or False.
Float: Decimal numbers (e.g., 2.9, 56.9, 0.0001).
Character or char: A single character (e.g., A, d).

Variables

Variables help us reference a piece of data for later use. They can hold any data type (e.g., strings, floats, integers, and Booleans).

Strings must be enclosed in single or double quotes.

Floats are numbers with a decimal point.

Examples:

# String
name = "Russell"  
# Integer
age = 45  
# Float
height = 170.8  
# Boolean
is_customer = False

Here, name, age, and height are variables. For example, when "name" is called, it references "Russell" because "Russell" has been assigned to name.

Rules for Variables:

Variables are defined with the equals sign (=).
Variables must start with a letter.
Variables can include numbers and underscores, but these cannot come at the beginning.
Variables are case-sensitive (e.g., Name and name are two different variables).
Spaces and special characters cannot be used in variable names.

Examples of Valid Variable Names:

footballers_names

ages45

Food

*Examples of Invalid Variable Names:
*
footballers-names (hyphen is a special character).

42age (variables cannot start with a number).

snow bunny (spaces are not allowed).

To know the value of a variable, use the print function:

name = "Russell"  
age = 45  
height = 170  

print(height)  
# Output: 170

Important: A variable itself does not require quotes.

Why is python essential for data analysts?

Jeornee — Tue, 10 Dec 2024 14:55:33 +0000

Python is a tool you’d love to work with as a data analyst for a variety of reasons: ease of use, extensive ecosystem of libraries and tools designed specifically for data analysis and visualization.

Let’s delve into the world of Python for a data analyst and why it’s essential.

1. Ease of learning and use:
Python has easy syntax which makes it accessible for beginners and very efficient for experienced analysts. Instead of dealing with difficult syntax, Python’s simplicity makes it easier for its users to focus on solving problems.

2. Data visualization tools:
With libraries such as
Matplotlib

Or

and Seaborn

in Python’s ecosystem, we have the options for detailed and aesthetically pleasing static visualization.
However, we still need dynamic visualization, and for that, we have Plotly and Bokeh.

3. Extensive library for data analysis:
As data analysts or aspiring data analysts, you’d agree that data manipulation is very important for analysis.

• Pandas: For data manipulation and analysis, especially with tabular data.

• NumPy: For numerical computations and handling multidimensional arrays.

• SciPy: For advanced statistical computations.

4. Large data handling:
Python can handle large datasets using frameworks such as PySpark.

5. Data cleaning and preparation:
Data cleaning is something you’ll have to do over and over again as a data analyst, and Python has tools that make this task faster and more efficient.

6. Automation:
Python supports automation of repetitive tasks, such as data extraction and loading.

7. Community support:
There’s a vast and active community of analysts, and this makes finding tutorials and resources easier for everyone.

In conclusion, Python is an effective tool for data analysis for its simple syntax, extensive libraries, visualization tools, large data handling capacity, data cleaning, data preparation, and automation of repetitive tasks.