Suppose I have a pandas data frame df:
I want to calculate the column wise mean of a data frame.
This is easy:
df.apply(average) then the column wise range max(col) - min(col). This is easy again:
df.apply(max) - df.apply(min) Now for each element I want to subtract its column's mean and divide by its column's range. I am not sure how to do that
Any help/pointers are much appreciated.
In [92]: df Out[92]: a b c d A -0.488816 0.863769 4.325608 -4.721202 B -11.937097 2.993993 -12.916784 -1.086236 C -5.569493 4.672679 -2.168464 -9.315900 D 8.892368 0.932785 4.535396 0.598124 In [93]: df_norm = (df - df.mean()) / (df.max() - df.min()) In [94]: df_norm Out[94]: a b c d A 0.085789 -0.394348 0.337016 -0.109935 B -0.463830 0.164926 -0.650963 0.256714 C -0.158129 0.605652 -0.035090 -0.573389 D 0.536170 -0.376229 0.349037 0.426611 In [95]: df_norm.mean() Out[95]: a -2.081668e-17 b 4.857226e-17 c 1.734723e-17 d -1.040834e-17 In [96]: df_norm.max() - df_norm.min() Out[96]: a 1 b 1 c 1 d 1 
A and B are part of a larger grouping factor that you want to normalize separately from C and D.
If you don't mind importing the sklearn library, I would recommend the method talked on this blog.
import pandas as pd from sklearn import preprocessing data = {'score': [234,24,14,27,-74,46,73,-18,59,160]} cols = data.columns df = pd.DataFrame(data) df min_max_scaler = preprocessing.MinMaxScaler() np_scaled = min_max_scaler.fit_transform(df) df_normalized = pd.DataFrame(np_scaled, columns = cols) df_normalized 
np_scaled = min_max_scaler.fit_transform(df.score.astype(float).values.reshape(-1, 1))You can use apply for this, and it's a bit neater:
import numpy as np import pandas as pd np.random.seed(1) df = pd.DataFrame(np.random.randn(4,4)* 4 + 3) 0 1 2 3 0 9.497381 0.552974 0.887313 -1.291874 1 6.461631 -6.206155 9.979247 -0.044828 2 4.276156 2.002518 8.848432 -5.240563 3 1.710331 1.463783 7.535078 -1.399565 df.apply(lambda x: (x - np.mean(x)) / (np.max(x) - np.min(x))) 0 1 2 3 0 0.515087 0.133967 -0.651699 0.135175 1 0.125241 -0.689446 0.348301 0.375188 2 -0.155414 0.310554 0.223925 -0.624812 3 -0.484913 0.244924 0.079473 0.114448 Also, it works nicely with groupby, if you select the relevant columns:
df['grp'] = ['A', 'A', 'B', 'B'] 0 1 2 3 grp 0 9.497381 0.552974 0.887313 -1.291874 A 1 6.461631 -6.206155 9.979247 -0.044828 A 2 4.276156 2.002518 8.848432 -5.240563 B 3 1.710331 1.463783 7.535078 -1.399565 B df.groupby(['grp'])[[0,1,2,3]].apply(lambda x: (x - np.mean(x)) / (np.max(x) - np.min(x))) 0 1 2 3 0 0.5 0.5 -0.5 -0.5 1 -0.5 -0.5 0.5 0.5 2 0.5 0.5 0.5 -0.5 3 -0.5 -0.5 -0.5 0.5 Slightly modified from: Python Pandas Dataframe: Normalize data between 0.01 and 0.99? but from some of the comments thought it was relevant (sorry if considered a repost though...)
I wanted customized normalization in that regular percentile of datum or z-score was not adequate. Sometimes I knew what the feasible max and min of the population were, and therefore wanted to define it other than my sample, or a different midpoint, or whatever! This can often be useful for rescaling and normalizing data for neural nets where you may want all inputs between 0 and 1, but some of your data may need to be scaled in a more customized way... because percentiles and stdevs assumes your sample covers the population, but sometimes we know this isn't true. It was also very useful for me when visualizing data in heatmaps. So i built a custom function (used extra steps in the code here to make it as readable as possible):
def NormData(s,low='min',center='mid',hi='max',insideout=False,shrinkfactor=0.): if low=='min': low=min(s) elif low=='abs': low=max(abs(min(s)),abs(max(s)))*-1.#sign(min(s)) if hi=='max': hi=max(s) elif hi=='abs': hi=max(abs(min(s)),abs(max(s)))*1.#sign(max(s)) if center=='mid': center=(max(s)+min(s))/2 elif center=='avg': center=mean(s) elif center=='median': center=median(s) s2=[x-center for x in s] hi=hi-center low=low-center center=0. r=[] for x in s2: if x<low: r.append(0.) elif x>hi: r.append(1.) else: if x>=center: r.append((x-center)/(hi-center)*0.5+0.5) else: r.append((x-low)/(center-low)*0.5+0.) if insideout==True: ir=[(1.-abs(z-0.5)*2.) for z in r] r=ir rr =[x-(x-0.5)*shrinkfactor for x in r] return rr This will take in a pandas series, or even just a list and normalize it to your specified low, center, and high points. also there is a shrink factor! to allow you to scale down the data away from endpoints 0 and 1 (I had to do this when combining colormaps in matplotlib:Single pcolormesh with more than one colormap using Matplotlib) So you can likely see how the code works, but basically say you have values [-5,1,10] in a sample, but want to normalize based on a range of -7 to 7 (so anything above 7, our "10" is treated as a 7 effectively) with a midpoint of 2, but shrink it to fit a 256 RGB colormap:
#In[1] NormData([-5,2,10],low=-7,center=1,hi=7,shrinkfactor=2./256) #Out[1] [0.1279296875, 0.5826822916666667, 0.99609375] It can also turn your data inside out... this may seem odd, but I found it useful for heatmapping. Say you want a darker color for values closer to 0 rather than hi/low. You could heatmap based on normalized data where insideout=True:
#In[2] NormData([-5,2,10],low=-7,center=1,hi=7,insideout=True,shrinkfactor=2./256) #Out[2] [0.251953125, 0.8307291666666666, 0.00390625] So now "2" which is closest to the center, defined as "1" is the highest value.
Anyways, I thought my application was relevant if you're looking to rescale data in other ways that could have useful applications to you.
This is how you do it column-wise:
[df[col].update((df[col] - df[col].min()) / (df[col].max() - df[col].min())) for col in df.columns] 