import pandas as pd
s = pd.Series(['A', 'B', 'A', 'C', 'A', 'B'])
print(s.value_counts())

A    3
B    2
C    1
dtype: int64

#DataManipulation #Transformation

---

21. series.unique()
Returns an array of unique values in a Series.

import pandas as pd
s = pd.Series(['A', 'B', 'A', 'C', 'A', 'B'])
print(s.unique())

['A' 'B' 'C']

---

22. df.sort_values()
Sorts a DataFrame by the values of one or more columns.

import pandas as pd
data = {'Name': ['Charlie', 'Alice', 'Bob'], 'Age': [35, 25, 30]}
df = pd.DataFrame(data)
sorted_df = df.sort_values(by='Age')
print(sorted_df)

Name  Age
1    Alice   25
2      Bob   30
0  Charlie   35

---

23. df.groupby()
Groups a DataFrame using a mapper or by a Series of columns for aggregation.

import pandas as pd
data = {'Dept': ['HR', 'IT', 'HR', 'IT'], 'Salary': [70, 85, 75, 90]}
df = pd.DataFrame(data)
grouped = df.groupby('Dept').mean()
print(grouped)

Salary
Dept        
HR      72.5
IT      87.5

---

24. df.agg()
Applies one or more aggregations over the specified axis.

import pandas as pd
data = {'Dept': ['HR', 'IT', 'HR', 'IT'], 'Salary': [70, 85, 75, 90]}
df = pd.DataFrame(data)
agg_results = df.groupby('Dept')['Salary'].agg(['mean', 'sum'])
print(agg_results)

mean  sum
Dept            
HR     72.5  145
IT     87.5  175

#Aggregation #Grouping #Sorting

---

25. df.apply()
Applies a function along an axis of the DataFrame.

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [10, 20, 30]})
# Apply a function to double each value in column 'A'
df['A_doubled'] = df['A'].apply(lambda x: x * 2)
print(df)

A   B  A_doubled
0  1  10          2
1  2  20          4
2  3  30          6

---

26. pd.merge()
Merges two DataFrames based on a common column or index, similar to a SQL join.

import pandas as pd
df1 = pd.DataFrame({'ID': [1, 2], 'Name': ['Alice', 'Bob']})
df2 = pd.DataFrame({'ID': [1, 2], 'Role': ['Engineer', 'Analyst']})
merged_df = pd.merge(df1, df2, on='ID')
print(merged_df)

ID   Name      Role
0   1  Alice  Engineer
1   2    Bob   Analyst

---

27. pd.concat()
Concatenates (stacks) pandas objects along a particular axis.

import pandas as pd
df1 = pd.DataFrame({'A': ['A0'], 'B': ['B0']})
df2 = pd.DataFrame({'A': ['A1'], 'B': ['B1']})
concatenated_df = pd.concat([df1, df2])
print(concatenated_df)

A   B
0  A0  B0
0  A1  B1

---

28. df.pivot_table()
Creates a spreadsheet-style pivot table as a DataFrame.

Top 30 MATLAB Image Processing Functions

#MATLAB #ImageProcessing #Basics

#1. imread()
Reads an image from a file into a matrix.

img = imread('peppers.png');
disp('Image "peppers.png" loaded into variable "img".');

Image "peppers.png" loaded into variable "img".

#2. imshow()
Displays an image in a figure window.

img = imread('peppers.png');
imshow(img);
title('Peppers Image');

Output: A new figure window opens, displaying the 'peppers.png' image with the title "Peppers Image".

#3. imwrite()
Writes an image matrix to a file.

img = imread('cameraman.tif');
imwrite(img, 'my_cameraman.jpg');
disp('Image saved as my_cameraman.jpg');

Image saved as my_cameraman.jpg

#4. size()
Returns the dimensions of the image matrix (rows, columns, color channels).

rgb_img = imread('peppers.png');
gray_img = imread('cameraman.tif');
size_rgb = size(rgb_img);
size_gray = size(gray_img);
disp(['Size of RGB image: ', num2str(size_rgb)]);
disp(['Size of grayscale image: ', num2str(size_gray)]);

Size of RGB image: 384   512     3
Size of grayscale image: 256   256

#5. rgb2gray()
Converts an RGB color image to a grayscale intensity image.

rgb_img = imread('peppers.png');
gray_img = rgb2gray(rgb_img);
imshow(gray_img);
title('Grayscale Peppers');

Output: A figure window displays the grayscale version of the peppers image.

---
#MATLAB #ImageProcessing #Conversion #Transformation

#6. im2double()
Converts an image to double-precision format, scaling data to the range [0, 1].

img_uint8 = imread('cameraman.tif');
img_double = im2double(img_uint8);
disp(['Max value of original image: ', num2str(max(img_uint8(:)))]);
disp(['Max value of double image: ', num2str(max(img_double(:)))]);

Max value of original image: 253
Max value of double image: 0.99216

#7. imresize()
Resizes an image to a specified size.

img = imread('cameraman.tif');
resized_img = imresize(img, 0.5); % Resize to 50% of original size
imshow(resized_img);
title('Resized Cameraman');

Output: A figure window displays the cameraman image at half its original size.

#8. imrotate()
Rotates an image by a specified angle.

img = imread('cameraman.tif');
rotated_img = imrotate(img, 30, 'bilinear', 'crop');
imshow(rotated_img);
title('Rotated 30 Degrees');

Output: A figure window displays the cameraman image rotated by 30 degrees, cropped to the original size.

#9. imcrop()
Crops an image to a specified rectangle.

img = imread('peppers.png');
% [xmin ymin width height]
cropped_img = imcrop(img, [100 80 250 200]);
imshow(cropped_img);
title('Cropped Image');

Output: A figure window displays only the rectangular section specified from the peppers image.

#10. rgb2hsv()
Converts an RGB image to the Hue-Saturation-Value (HSV) color space.

rgb_img = imread('peppers.png');
hsv_img = rgb2hsv(rgb_img);
hue_channel = hsv_img(:,:,1); % Extract the Hue channel
imshow(hue_channel);
title('Hue Channel of Peppers Image');

Output: A figure window displays the Hue channel of the peppers image as a grayscale image.

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#MATLAB #ImageProcessing #Enhancement

#11. imhist()
Displays the histogram of an image, showing the distribution of pixel intensity values.