## How Normalize Data Mining in Python with library

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How Normalize Data Mining MinMax from csv in Python 3 with library this is example of my data

```RT      NK    NB    SU    SK    P    TNI IK   IB     TARGET
84876   902  1192  2098  3623  169   39  133  1063   94095
79194   902  1050  2109  3606  153   39  133   806   87992
75836   902  1060  1905  3166  161   39  133   785   83987
75571   902   112  1878  3190  158   39  133   635   82618
83797  1156   134  1900  3518  218   39  133   709   91604
91648  1291   127  2225  3596  249   39  133   659   99967
79063  1346   107  1844  3428  247   39  133   591   86798
84357  1018   122  2152  3456  168   39  133   628   92073
90045   954   110  2044  3638  174   39  133   734   97871
83318   885   198  1872  3691  173   39  133   778   91087
93300  1044   181  2077  4014  216   39  133   635  101639
88370  1831   415  2074  4323  301   39  133   502   97988
91560  1955   377  2015  4153  349   39  223   686  101357
85746  1791   314  1931  3878  297   39  215   449   94660
93855  1891   344  2064  3947  287   39  162   869  103458
97403  1946   382  1937  4029  289   39  122  1164  107311
```

the formula MinMax is

```= (data-min)/(max-min)*0.8+0.1
```

i got the code but the normalize data is not each column

I know how to count it like this

```(first data of RT - min column RT data) / (max column RT- min column RT) * 0.8 + 0.1, etc
```

so does the next column

```(first data of NK - min column NK data) / (max column NK- min column NK) * 0.8 + 0.1
```

this is my code, but i don't understand

```from sklearn.preprocessing import Normalizer
from numpy import set_printoptions
import pandas as pd

#print(df1)

namaFile = 'dataset.csv'
nama = ['rt', 'niagak', 'niagab', 'sosum', 'soskhus', 'p', 'tni', 'ik', 'ib', 'TARGET']
array = dataFrame.values

#membagi array
X = array[:,0:10]
Y = array[:,9]

skala = Normalizer().fit(X)
normalisasiX = skala.transform(X)

#data hasil
print('Normalisasi Data')
set_printoptions(precision = 3)
print(normalisasiX[0:5,:])
```

the results of manual counting with code are very different

we can use pandas python library.

```import pandas as pd

norm = (df - df.min()) / (df.max() - df.min() )*0.8 + 0.1
```

norm will have the normalised dataframe

Rescaling Data for Machine Learning in Python with Scikit-Learn, The example below demonstrate data normalization of the Iris that you can use to rescale your data in Python using the scikit-learn library. Standardize or Normalize? — Examples in Python. Robert R.F. DeFilippi. Follow. Apr 29, 2018 · 6 min read. A common misconception is between what it is — and when to — standardize data

By using MinMaxScaler from sklearn you can solve your problem.

```from pandas import read_csv
from sklearn.preprocessing import MinMaxScaler

data = df.values

scaler = MinMaxScaler()
scaler.fit(data)

scaled_data = scaler.transform(data)
```

Data Normalization in Python, It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to� Data rescaling is an important part of data preparation before applying machine learning algorithms. In this post you discovered where data rescaling fits into the process of applied machine learning and two methods: Normalization and Standardization that you can use to rescale your data in Python using the scikit-learn library.

```import matplotlib.pyplot as plt
import pandas as pd
from sklearn.cluster import KMeans
from pandas import DataFrame
from sklearn.preprocessing import MinMaxScaler

#print(data)
df = DataFrame(data,columns=['X','Y'])
scaler = MinMaxScaler()
scaler.fit(df)
#print(scaler.transform(df))
minmaxdf = scaler.transform(df)
kmeans = KMeans(n_clusters=2).fit(minmaxdf)
centroids = kmeans.cluster_centers_
plt.scatter(df['X'], df['Y'], c= kmeans.labels_.astype(float), s=30, alpha=1)
```

You can use the code I wrote above. I performed min-max normalization on two-dimensional data and then applied K means clustering algorithm.Be sure to include your own data set in .csv format

Standardize or Normalize? — Examples in Python, The use of a normalization method will improve analysis from multiple models. Additionally, if we were to use any algorithms on this data set� normalize function. normalize is a function present in sklearn. preprocessing package. Normalization is used for scaling input data set on a scale of 0 to 1 to have unit norm. Norm is nothing but calculating the magnitude of the vector. Syntax: sklearn.preprocessing.normalize(data,norm) Parameter: data:- like input array or matrix of the data set.

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Data Normalization in Python, Why not just dedicate an entire post to normalizing data! collection using R ( comes from a previous post on the MLB), but I wanted to do the analysis in Rodeo. library(feather) write_feather(standings, "standings.feather")� Our Data. The data I’m using is a collection of MLB standings and attendance data from the past 70 years. You can read more about how I collected it in this post. I’m sure a lot of you saw the news last week about feather, the brainchild from Wes McKinney and Hadley Wickham. As both a Python and an R user, I think it’s a really compelling

Data Cleaning and Preprocessing for Beginners, The absolutely first thing you need to do is to import libraries for data preprocessing. the most popular and important Python libraries for working on data are Numpy, set is important, to avoid mistakes in the data analysis and the modeling process. Some algorithms like SVM converge far faster on normalized data, so it� Tags: Data Preparation, Data Preprocessing, NLP, Python, Text Analytics, Text Mining This post will serve as a practical walkthrough of a text data preprocessing task using some common Python tools. By Matthew Mayo , KDnuggets.

• add `df = df.astype(float)` after `read_csv()` to cast your dataframe to float