First Lets talk about csv
Its a plain text format which comes from (Comma Separated Values)
Most spreadsheet application supports this. You can open and edit it from excl, Libra office cal , google sheets and other spread sheet application, also supported by most of the programming language.
For simplicity and flexibility often used in data science.
Create Our first csv file
We will create our csv file with as simple way as possible. Even we will not use any spreadsheet application.
Open any text editor. You can open vs code or geany or your beloved code editor.
In the text editor. create and save a new file named colors.cvs Make sure you properly added the .csv extinction.
Now add this text in the file.
Color,Red,Green,Blue,Hex Code
Red,255,0,0,#FF0000
Green,0,255,0,#00FF00
Blue,0,0,255,#0000FF
Yellow,255,255,0,#FFFF00
Orange,255,165,0,#FFA500
Purple,128,0,128,#800080
Cyan,0,255,255,#00FFFF
Magenta,255,0,255,#FF00FF
Pink,255,192,203,#FFC0CB
Lime,0,255,0,#00FF00
Teal,0,128,128,#008080
Brown,165,42,42,#A52A2A
Maroon,128,0,0,#800000
Navy,0,0,128,#000080
Olive,128,128,0,#808000
Sky Blue,135,206,235,#87CEEB
Gold,255,215,0,#FFD700
Silver,192,192,192,#C0C0C0
save it . That’s all.
Now we will read the csv file from python.
Before that we need to be clear about python file mode
| Mode | Description |
|---|---|
| ‘r’ | Read-only mode. (Read): Open a file to only read its contents. |
| ‘w’ | Write-only mode. (Write): Create a new file or completely rewrite an existing one. (erases old data!) |
| ‘a’ | (Append): The data is written to the end of the file. |
| ‘r+,’ ‘w+,’ ‘a+’ | Read/write mode. These allow both reading and writing, with variations on how existing data is handled |
Python have build in csv module.
To read csv file we will use
csv.reader
csv.DictReader
csv.reader
- Turns each row of your CSV file into a list of the individual values.
- Example: [‘Sky Blue’, ‘135’, ‘206’, ‘235’, ‘#87CEEB’]
csv.DictReader
- Turns each row of your CSV into a dictionary (like a little address book).
- Uses the first row of the CSV as keys (headers) for the dictionaries.
- Example: {‘Color’: ‘Sky Blue’, ‘Red’: ‘135’, ‘Green’: ‘206’, ‘Blue’: ‘235’, ‘Hex Code’: ‘#87CEEB’}
Now open our colors.csv with python
Example with csv.reader:
The csv.reader function turns each row of the CSV file into a list of individual values. The example
import csv
with open('colors.csv', 'r', newline='') as csvfile:
reader = csv.reader(csvfile)
next(reader)
for row in reader:
print(row)
color_name = row[0]
red_value = int(row[1])
green_value = int(row[2])
blue_value = int(row[3])
hex_code = row[4]
# We can now process the color data as needed
print(f"Color: {color_name} (R: {red_value}, G: {green_value}, B: {blue_value}), Hex Code: {hex_code}")
csv.DictReader
The csv.DictReader function turns each row of the CSV file into a dictionary, using the first row as keys (headers). Example:
import csv
with open('colors.csv', 'r', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
print(row)
color_name = row['Color']
red_value = int(row['Red'])
green_value = int(row['Green'])
blue_value = int(row['Blue'])
hex_code = row['Hex Code']
print(f"Color: {color_name} (R: {red_value}, G: {green_value}, B: {blue_value}), Hex Code: {hex_code}")
csv writer
The csv.writer function allows you to write data from lists to a CSV file. Example:
import csv
colors_to_write = [
['White', 255, 255, 255, '#FFFFFF'],
['Black', 0, 0, 0, '#000000'],
['Gold', 255, 215, 0, '#FFD700']
]
with open('new_colors.csv', 'w', newline='') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['Color', '255', '0', '0', '#FF0000']) # Write the header row
writer.writerows(colors_to_write) # Write the data rows
CSV DictWriter
The csv.DictWriter function allows you to write data from dictionaries to a CSV file. Example:
import csv
colors_to_write = [
{'Color': 'White', '255': 255, '0': 255, '#FF0000': '#FFFFFF'},
{'Color': 'Black', '255': 0, '0': 0, '#FF0000': '#000000'},
{'Color': 'Gold', '255': 255, '0': 215, '#FF0000': '#FFD700'},
]
with open('new_colors_dict.csv', 'w', newline='') as csvfile:
fieldnames = ['Color', '255', '0', '#FF0000']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader() # Write the header row
writer.writerows(colors_to_write) # Write the data rows
Now a practical approach . Analysis our csv data
import csv
import matplotlib.pyplot as plt
filename = 'colors.csv'
red_values = []
green_values = []
blue_values = []
with open(filename, 'r', newline='') as csvfile:
reader = csv.reader(csvfile)
next(reader)
for row in reader:
red_values.append(int(row[1]))
green_values.append(int(row[2]))
blue_values.append(int(row[3]))
# Visualize histograms of red, green, and blue values
fig, axs = plt.subplots(3)
fig.suptitle('Histograms of RGB Values')
axs[0].hist(red_values, bins=20, color='red')
axs[0].set_xlabel('Red Value')
axs[0].set_ylabel('Frequency')
axs[1].hist(green_values, bins=20, color='green')
axs[1].set_xlabel('Green Value')
axs[1].set_ylabel('Frequency')
axs[2].hist(blue_values, bins=20, color='blue')
axs[2].set_xlabel('Blue Value')
axs[2].set_ylabel('Frequency')
plt.show()
