i am using from sklearn.preprocessing import MinMaxScaler with following code and dataset:
df = pd.DataFrame({
"A" : [-0.5624105,
-0.5637749,
0.2973856,
0.619784,
0.007297921,
0.8146919,
0.1082434,
-0.2311236,
-0.6945567,
-0.6807524,
-0.1017431,
0.5889628,
0.5384794,
0.3906553,
0.3843442,
0.4408366,
0.4035791,
0.05258237,
-0.4847771
],
"B" : [-0.5068743,
0.1422121,
0.6444226,
0.4959088,
-0.2260773,
0.3420533,
0.2346546,
0.1177824,
-0.7701161,
-0.7566853,
-0.5091485,
0.4509938,
0.4209853,
0.304058,
0.3753832,
0.6958977,
0.6763205,
0.05536954,
-0.9857719
]})
min_max_scaler = MinMaxScaler(feature_range=(0,255))
print(df)
df[df.columns] = min_max_scaler.fit_transform(df[df.columns])
print(df)
print(type(df))
i want to scale it with the smallest value in the whole dataset and biggest value in whole dataset how can I manage this using the same code? is it possible?
A B
0 -0.562411 -0.506874
1 -0.563775 0.142212
2 0.297386 0.644423
3 0.619784 0.495909
4 0.007298 -0.226077
5 0.814692 0.342053
6 0.108243 0.234655
7 -0.231124 0.117782
8 -0.694557 -0.770116
9 -0.680752 -0.756685
10 -0.101743 -0.509149
11 0.588963 0.450994
12 0.538479 0.420985
13 0.390655 0.304058
14 0.384344 0.375383
15 0.440837 0.695898
16 0.403579 0.676320
17 0.052582 0.055370
18 -0.484777 -0.985772
A B
0 22.327190 72.617646
1 22.096664 171.041874
2 167.596834 247.194572
3 222.068703 224.674680
4 118.584127 115.196304
5 255.000000 201.344798
6 135.639699 185.059394
7 78.300845 167.337476
8 0.000000 32.700971
9 2.332350 34.737551
10 100.160748 72.272798
11 216.861207 217.863993
12 208.331620 213.313653
13 183.355519 195.583380
14 182.289206 206.398778
15 191.834063 255.000000
16 185.539101 252.031411
17 126.235309 157.873501
18 35.443994 0.000000
i don't want different mapping for each column i need to map it using -0.985772 0.814692 (column b row 18, column a row 5)
You have 2 ways to do this:
# Manually:
min_value, max_value = df.min().min(), df.max().max()
scaled1 = (df - min_value) * 255 / (max_value - min_value)
# Using MinMaxScaler
min_max_scaler = MinMaxScaler(feature_range=(0,255))
# Stack everything into a single column to scale by the global min / max
tmp = df.to_numpy().reshape(-1,1)
scaled2 = min_max_scaler.fit_transform(tmp).reshape(len(df), 2)
Both return the same result:
np.isclose(scaled1, scaled2).all()
# True
You can make a new DataFrame with the scaled values:
scaled = pd.DataFrame(scaled1, index=df.index, columns=df.columns)
Or assign them back to df:
df.loc[:] = scaled1