Chat GPT: Currency Analysis: Symbols & Distribution

Code Interpreter

currency.csv

Create some analysis of this data

Alright, I'll start by loading the data to understand its structure and content. After examining the dataset, I'll then provide some initial insights and analysis.

Finished working

python
import pandas as pd

# Load the CSV file into a DataFrame
currency_data = pd.read_csv('/mnt/data/currency.csv')

# Display the first few rows of the data to get an overview
currency_data.head()

RESULT

  Code Symbol                      Name
0  AED    د.إ    United Arab Emirates d
1  AFN      ؋            Afghan afghani
2  ALL      L              Albanian lek
3  AMD    AMD             Armenian dram
4  ANG      ƒ  Netherlands Antillean gu

The dataset appears to contain information about different currencies. The columns are:

Code: The international code for the currency.
Symbol: The symbol used to represent the currency.
Name: The full name or description of the currency.

To provide a meaningful analysis, I'll check the following:

The total number of unique currencies listed.
The most common currency symbols (in case some symbols are shared across multiple currencies).
A count of currencies by their first letter (to get an idea of how currencies are distributed alphabetically).

Let's begin with the first point.

Finished working

python
# Count the total number of unique currencies based on their codes
unique_currencies = currency_data['Code'].nunique()

unique_currencies

RESULT

There are a total of 163 unique currencies listed in the dataset based on their codes.

Next, let's explore the most common currency symbols to see if any symbols are shared across multiple currencies.

Finished working

python
# Get the count of occurrences of each symbol
symbol_counts = currency_data['Symbol'].value_counts()

# Display the most common currency symbols
common_symbols = symbol_counts[symbol_counts > 1]
common_symbols

RESULT

$      28
£       8
Fr      7
L       4
₨       4
Br      3
Sh      2
₩       2
kr      2
P       2
﷼       2
฿       2
¥       2
CFA     2
Name: Symbol, dtype: int64

Several currency symbols are shared across multiple currencies:

$: Used by 28 currencies
£: Used by 8 currencies
Fr: Used by 7 currencies
L: Used by 4 currencies
₨: Used by 4 currencies
Br: Used by 3 currencies

... and so on.

It's important to note that while these symbols might be the same, the currencies they represent in different countries or regions can be quite distinct. For example, the dollar sign "$" is used for the US Dollar, Canadian Dollar, Australian Dollar, etc., but each of these is a different currency with different values and characteristics.

Lastly, let's analyze the distribution of currencies based on the first letter of their names. This will give us an idea of how currencies are distributed alphabetically.

Finished working

python
# Get the first letter of each currency name and count occurrences
first_letter_counts = currency_data['Name'].str[0].value_counts().sort_index()

first_letter_counts

RESULT

A     9
B    19
C    16
D     3
E     5
F     2
G     8
H     4
I     7
J     4
K     4
L     5
M    14
N     9
O     1
P     7
Q     1
R     3
S    21
T     9
U     6
V     3
W     1
Y     1
Z     1
Name: Name, dtype: int64

The distribution of currencies based on the first letter of their names is as follows:

Currencies beginning with the letter S are the most common, with 21 currencies.
This is followed by currencies starting with the letter B (19 currencies) and C (16 currencies).
M also has a significant number with 14 currencies.

On the other hand, some letters like O, Q, W, Y, and Z each have only one currency associated with them.

This kind of distribution can be better visualized using a bar chart. Would you like me to create one for you?

Can you analyze the data with Python?

Of course! We've been analyzing the data using Python so far. I can perform additional analyses or visualizations based on the dataset and your requirements. Here are a few things we can do:

Visualization: Display visual plots like bar charts, pie charts, etc., to understand the distribution of data better.
Descriptive Statistics: Provide statistics like mean, median, mode, etc., if applicable.
Data Cleaning: Check for missing values or potential data inconsistencies and address them.
Advanced Analysis: Dive deeper to find patterns, correlations, or other insights.

Please specify what kind of analysis or visualization you would like, or if you have any specific questions about the dataset.

But why I don't see any Python code?

I apologize for the confusion. You should have been seeing the Python code I've executed to analyze the data so far. If you haven't, there might be an issue with the display.

To ensure you can see the code, I'll provide a summary of what I've done and continue with any additional analysis you'd like to see.

Here's a recap of the Python actions taken:

Loaded the CSV file into a DataFrame using pandas.
Displayed the first few rows of the dataset to understand its structure.
Counted the total number of unique currencies based on their codes.
Identified the most common currency symbols and how many times each is used.
Analyzed the distribution of currencies based on the first letter of their names.

Please let me know how you'd like to proceed, and I'll ensure the Python code is visible to you.