one easy way by using Pandas: (here I want to use mean normalization)

normalized_df=(df-df.mean())/df.std() 

to use min-max normalization:

normalized_df=(df-df.min())/(df.max()-df.min()) 

Edit: To address some concerns, need to say that Pandas automatically applies colomn-wise function in the code above.

Detailed Example of Normalization Methods

• Pandas normalization (unbiased)
• Sklearn normalization (biased)
• Does biased-vs-unbiased affect Machine Learning?
• Mix-max scaling

References: Wikipedia: Unbiased Estimation of Standard Deviation

Example Data

import pandas as pd df = pd.DataFrame({ 'A':[1,2,3], 'B':[100,300,500], 'C':list('abc') }) print(df) A B C 0 1 100 a 1 2 300 b 2 3 500 c 

Normalization using pandas (Gives unbiased estimates)

When normalizing we simply subtract the mean and divide by standard deviation.

df.iloc[:,0:-1] = df.iloc[:,0:-1].apply(lambda x: (x-x.mean())/ x.std(), axis=0) print(df) A B C 0 -1.0 -1.0 a 1 0.0 0.0 b 2 1.0 1.0 c 

Normalization using sklearn (Gives biased estimates, different from pandas)

If you do the same thing with sklearn you will get DIFFERENT output!

import pandas as pd from sklearn.preprocessing import StandardScaler scaler = StandardScaler() df = pd.DataFrame({ 'A':[1,2,3], 'B':[100,300,500], 'C':list('abc') }) df.iloc[:,0:-1] = scaler.fit_transform(df.iloc[:,0:-1].to_numpy()) print(df) A B C 0 -1.224745 -1.224745 a 1 0.000000 0.000000 b 2 1.224745 1.224745 c 

Does Biased estimates of sklearn makes Machine Learning Less Powerful?

NO.

The official documentation of sklearn.preprocessing.scale states that using biased estimator is UNLIKELY to affect the performance of machine learning algorithms and we can safely use them.

From official documentation:

We use a biased estimator for the standard deviation, equivalent to numpy.std(x, ddof=0). Note that the choice of ddof is unlikely to affect model performance.

What about MinMax Scaling?

There is no Standard Deviation calculation in MinMax scaling. So the result is same in both pandas and scikit-learn.

import pandas as pd df = pd.DataFrame({ 'A':[1,2,3], 'B':[100,300,500], }) (df - df.min()) / (df.max() - df.min()) A B 0 0.0 0.0 1 0.5 0.5 2 1.0 1.0 # Using sklearn from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() arr_scaled = scaler.fit_transform(df) print(arr_scaled) [[0. 0. ] [0.5 0.5] [1. 1. ]] df_scaled = pd.DataFrame(arr_scaled, columns=df.columns,index=df.index) print(df_scaled) A B 0 0.0 0.0 1 0.5 0.5 2 1.0 1.0 

Based on this post: https://stats.stackexchange.com/questions/70801/how-to-normalize-data-to-0-1-range

You can do the following:

def normalize(df): result = df.copy() for feature_name in df.columns: max_value = df[feature_name].max() min_value = df[feature_name].min() result[feature_name] = (df[feature_name] - min_value) / (max_value - min_value) return result 

You don't need to stay worrying about whether your values are negative or positive. And the values should be nicely spread out between 0 and 1.

Your problem is actually a simple transform acting on the columns:

def f(s): return s/s.max() frame.apply(f, axis=0) 

Or even more terse:

 frame.apply(lambda x: x/x.max(), axis=0) 

If you like using the sklearn package, you can keep the column and index names by using pandas loc like so:

from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() scaled_values = scaler.fit_transform(df) df.loc[:,:] = scaled_values 

Take care with this answer, as it ONLY works for data that ranges [0, n]. This does not work for any range of data.

Simple is Beautiful:

df["A"] = df["A"] / df["A"].max() df["B"] = df["B"] / df["B"].max() df["C"] = df["C"] / df["C"].max() 

You can create a list of columns that you want to normalize

column_names_to_normalize = ['A', 'E', 'G', 'sadasdsd', 'lol'] x = df[column_names_to_normalize].values x_scaled = min_max_scaler.fit_transform(x) df_temp = pd.DataFrame(x_scaled, columns=column_names_to_normalize, index = df.index) df[column_names_to_normalize] = df_temp 

Your Pandas Dataframe is now normalized only at the columns you want

However, if you want the opposite, select a list of columns that you DON'T want to normalize, you can simply create a list of all columns and remove that non desired ones

column_names_to_not_normalize = ['B', 'J', 'K'] column_names_to_normalize = [x for x in list(df) if x not in column_names_to_not_normalize ] 

Normalize

You can use minmax_scale to transform each column to a scale from 0-1.

from sklearn.preprocessing import minmax_scale df[:] = minmax_scale(df) 

Standardize

You can use scale to center each column to the mean and scale to unit variance.

from sklearn.preprocessing import scale df[:] = scale(df) 

Column Subsets

Normalize single column

from sklearn.preprocessing import minmax_scale df['a'] = minmax_scale(df['a']) 

Normalize only numerical columns

import numpy as np from sklearn.preprocessing import minmax_scale cols = df.select_dtypes(np.number).columns df[cols] = minmax_scale(df[cols]) 

Full Example

# Prep import pandas as pd import numpy as np from sklearn.preprocessing import minmax_scale # Sample data df = pd.DataFrame({'a':[0,1,2], 'b':[-10,-30,-50], 'c':['x', 'y', 'z']}) # MinMax normalize all numeric columns cols = df.select_dtypes(np.number).columns df[cols] = minmax_scale(df[cols]) # Result print(df) # a b c # 0 0.0 1.0 x # 2 0.5 0.5 y # 3 1.0 0.0 z 

Notes:

In all examples scale can be used instead of minmax_scale. Keeps index, column names or non-numerical variables unchanged. Function is applied for each column.

Caution:

For machine learning, use minmax_scale or scale after train_test_split to avoid data leakage.

Info

More info on standardization and normalization:

• https://machinelearningmastery.com/standardscaler-and-minmaxscaler-transforms-in-python/
• https://en.wikipedia.org/wiki/Normalization_(statistics)
• https://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing

I think that a better way to do that in pandas is just

df = df/df.max().astype(np.float64) 

Edit If in your data frame negative numbers are present you should use instead

df = df/df.loc[df.abs().idxmax()].astype(np.float64) 

The solution given by Sandman and Praveen is very well. The only problem with that if you have categorical variables in other columns of your data frame this method will need some adjustments.

My solution to this type of issue is following:

 from sklearn import preprocesing x = pd.concat([df.Numerical1, df.Numerical2,df.Numerical3]) min_max_scaler = preprocessing.MinMaxScaler() x_scaled = min_max_scaler.fit_transform(x) x_new = pd.DataFrame(x_scaled) df = pd.concat([df.Categoricals,x_new]) 

You might want to have some of columns being normalized and the others be unchanged like some of regression tasks which data labels or categorical columns are unchanged So I suggest you this pythonic way (It's a combination of @shg and @Cina answers ):

features_to_normalize = ['A', 'B', 'C'] # could be ['A','B'] df[features_to_normalize] = df[features_to_normalize].apply(lambda x:(x-x.min()) / (x.max()-x.min())) 

It is only simple mathematics. The answer should as simple as below.

normed_df = (df - df.min()) / (df.max() - df.min()) 

df_normalized = df / df.max(axis=0) 

You can simply use the pandas.DataFrame.transform1 function in this way:

df.transform(lambda x: x/x.max()) 

This is how you do it column-wise using list comprehension:

[df[col].update((df[col] - df[col].min()) / (df[col].max() - df[col].min())) for col in df.columns] 

def normalize(x): try: x = x/np.linalg.norm(x,ord=1) return x except : raise data = pd.DataFrame.apply(data,normalize) 

From the document of pandas,DataFrame structure can apply an operation (function) to itself .

DataFrame.apply(func, axis=0, broadcast=False, raw=False, reduce=None, args=(), **kwds) 

Applies function along input axis of DataFrame. Objects passed to functions are Series objects having index either the DataFrame’s index (axis=0) or the columns (axis=1). Return type depends on whether passed function aggregates, or the reduce argument if the DataFrame is empty.

You can apply a custom function to operate the DataFrame .

The following function calculates the Z score:

def standardization(dataset): """ Standardization of numeric fields, where all values will have mean of zero and standard deviation of one. (z-score) Args: dataset: A `Pandas.Dataframe` """ dtypes = list(zip(dataset.dtypes.index, map(str, dataset.dtypes))) # Normalize numeric columns. for column, dtype in dtypes: if dtype == 'float32': dataset[column] -= dataset[column].mean() dataset[column] /= dataset[column].std() return dataset 

New Scikit-Learn (Version>=1.2): Keeps DataFrame Column Names

In the new version of scikit-learn, it is now actually possible to keep the pandas column names intact even after the transform, below is an example:

>>> import pandas as pd >>> from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler >>> df = pd.DataFrame({'col1':[1000, 765, 800], 'col2':[10, 5, 7], 'col3':[0.5, 0.35, 0.09]}, ) >>> df.head(3) col1 col2 col3 0 1000 10 0.50 1 765 5 0.35 2 800 7 0.09 >>> scaler = MaxAbsScaler().set_output(transform="pandas") #change here >>> scaler.fit(df) >>> df_scaled = scaler.transform(df) >>> df_scaled.head(3) col1 col2 col3 0 1.000 1.0 1.00 1 0.765 0.5 0.70 2 0.800 0.7 0.18 

I wrote a summary of the new updates here and you can also check the scikit-learn release highlights page.

Also, personally have never been a big fan of MaxAbsScaler, but I went with this one to answer op's question.

Hope this helps, cheers!!

You can do this in one line

DF_test = DF_test.sub(DF_test.mean(axis=0), axis=1)/DF_test.mean(axis=0) 

it takes mean for each of the column and then subtracts it(mean) from every row(mean of particular column subtracts from its row only) and divide by mean only. Finally, we what we get is the normalized data set.

Pandas does column wise normalization by default. Try the code below.

X= pd.read_csv('.\\data.csv') X = (X-X.min())/(X.max()-X.min()) 

The output values will be in range of 0 and 1.

Hey use the apply function with lambda which speeds up the process:

def normalize(df_col): # Condition to exclude 'ID' and 'Class' feature if (str(df_col.name) != str('ID') and str(df_col.name)!=str('Class')): max_value = df_col.max() min_value = df_col.min() #It avoids NaN and return 0 instead if max_value == min_value: return 0 sub_value = max_value - min_value return np.divide(np.subtract(df_col,min_value),sub_value) else: return df_col df_normalize = df.apply(lambda x :normalize(x)) 

To normalise a DataFrame column, using only native Python.

Different values influence processes, e.g. plot colours.

Between 0 and 1:

min_val = min(list(df['col'])) max_val = max(list(df['col'])) df['col'] = [(x - min_val) / max_val for x in df['col']] 

Between -1 to 1:

df['col'] = [float(i)/sum(df['col']) for i in df['col']] 

OR

df['col'] = [float(tp) / max(abs(df['col'])) for tp in df['col']]