CODEWITHSUNDEEP
pythonpandasdataframesklearn-pandas
Nightingale
Nightingale

Reputation: 143

Min Max scaling for whole dataframe python

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)

Upvotes: 6

Views: 3743

Answers (1)

Code Different
Code Different

Reputation: 93151

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

Upvotes: 4

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