## How encode categorical data without affecting numerical data in a DataFrame?

labelencoder

one hot encoding

how to convert categorical data to numerical data in python pandas

pandas convert categorical into numeric

get_dummies

handling categorical data in python

onehotencoder multiple columns

Loan_ID Gender Married Dependents Education ApplicantIncome 1 LP001003 Male Yes 1 Graduate 4583 2 LP001005 Male Yes 0 Graduate 3000 3 LP001006 Male Yes 0 Not Graduate 2583 4 LP001008 Male No 0 Graduate 6000 5 LP001011 Male Yes 2 Graduate 5417

How to encode 'Gender', 'Married', 'Education' columns without affecting 'Loan_ID', 'Dependents','ApplicantIncome' columns.

This should solve your problem.

from sklearn.preprocessing import LabelEncoder le = LabelEncoder() for cat_var in ['Gender', 'Married', 'Education']: df[cat_var] = le.fit_transform(df[cat_var])

**Guide to Encoding Categorical Values in Python,** As with many other aspects of the Data Science world, there is no single answer on that can be applied to transform the categorical data into suitable numeric values. variables, we are going to include only the object columns in our dataframe. PCA is one method for reducing dimensionality of data. When carelessly encoding categorical features to numerical ones, a tree-based algorithm could improperly split the data, thinking there exists certain orders within the encoded data. One-hot-encoding

I prefer to use `pd.get_dummies`

method, so:

ohe_df = pd.get_dummies(df, columns=['Gender', 'Married', 'Education'])

**Categorical encoding using Label-Encoding and One-Hot-Encoder ,** In Machine Learning, convert categorical data into numerical data using With this, we completed the label-encoding of variable bridge-type. Apparently, there is no relation between various bridge type, but DataFrame(bridge_types, columns=['Bridge_Types'])# converting type of columns to 'category' Another simplest way to encode Ordinal Categorical data, is to find the replace the value for each label, that should satisfy the intrinsic ordering among them. Let’s replace the values in the

While preparing your data consider few things:

LoanID column is ordinal Categorical data and it needs to be converted to numerical using one hot encoding as algorithms only understand numerics

Label encoder works well for binary class for multi class try using one hot encoder or factorize

Create separate columns for numerical and converted categorical data and concat in one df for training and test split

As an example to your question:

#create ndarray for label encodoing (sklearn) Gender = data.iloc[:,1:2].values Married = data.iloc[:,2:3].values Education = data.iloc[:,4:3].values ## le for Gender le = LabelEncoder() Gender[:,0] = le.fit_transform(Gender[:,0]) Gender = pd.DataFrame(Gender) Gender.columns = ['Gender'] le_Gender_mapping = dict(zip(le.classes_, le.transform(le.classes_))) print("Sklearn label encoder results for Gender:") print(le_Gender_mapping) **Do the same for 'Married' and 'Education' as they are also binary Load_ID = data.iloc[:,0:1].values #ndarray ## ohe for Loan_ID ohe = OneHotEncoder() Load_ID = ohe.fit_transform(Loan_ID).toarray() Load_ID = pd.DataFrame(Load_ID) print("Sklearn one hot encoder results for Load_ID:") ##put data together X_num = data[['Applicant_Income']].copy() X_final = pd.concat([Loan_ID, Gender, Married, Education, X_num], axis = 1) This prepares your initial data set, take out column you want to predict as y_final and do the train test split. Note: After train test split do Normalize or Standardize(preferred as less affected by outliers) otherwise the Applicant_income will dominate the predictions

**(Tutorial) Handling Categorical Data in Python,** Identifying Categorical Data: Nominal, Ordinal and Continuous labeled without any order of precedence are called nominal features. You can get the total number of missing values in the DataFrame by the following one liner code: Another approach is to encode categorical values with a technique� A one hot encoding is appropriate for categorical data where no relationship exists between categories. It involves representing each categorical variable with a binary vector that has one element for each unique label and marking the class label with a 1 and all other elements 0.

You can use Label Encoder:

from sklearn import preprocessing le1 = preprocessing.LabelEncoder() df['Gender'] =le1.fit_transform(df['Gender']) le2 = preprocessing.LabelEncoder() df['Married'] =le2.fit_transform(df['Married']) le3 = preprocessing.LabelEncoder() df['Education'] =le3.fit_transform(df['Education'])

This approach will use a different label encoder for every column, that will also mean that you will have the same number in different columns.

When you run one label encoder for all, the number will be only the same if it exactly the same word.

After your classification, you can inverse the labels with:

df['Married'] = le2.inverse_transform(df['Married']

**3 Ways to Encode Categorical Variables for Deep Learning,** How to Encode Categorical Data for Deep Learning in Keras classifies breast cancer patient data as either a recurrence or no recurrence of cancer. load the dataset as a pandas DataFrame Each embedding also requires the number of dimensions to use for the I hope this is not altering the model. In many Machine-learning or Data Science activities, the data set might contain text or categorical values (basically non-numerical values). For example, color feature having values like red, orange, blue, white etc. Meal plan having values like breakfast, lunch, snacks, dinner, tea etc. Few algorithms such as CATBOAST, decision-trees can handle categorical values very well but most of the

**Encoding categorical variables,** Non-numeric features generally have to be encoded into one or Many machine learning algorithms are not able to use non-numeric data. Categorical: If the levels are just different without an ordering, we call the feature categorical. seen in the test set, and can return a dataframe with named columns. LabelEncoder and OneHotEncoder is usually need to be used together as a two steps method to encode categorical features. LabelEncoder outputs a dataframe type while OneHotEncoder outputs a numpy array. OneHotEncoder has the option to output a sparse matrix. DictVectorizer is a one step method to encode and support sparse matrix output.

**Encoding Categorical data in Machine Learning,** Categorical data that are having any intrinsic ordering among So the Categorical data must be transformed or encoded into Numerical type before Checking for null values in the modified dataframe using pandas isnull() method. If a whole dataframe is encoded, then there will no issue directly it will� I try to encode a number of columns containing categorical data ("Yes" and "No") in a large pandas dataframe. The complete dataframe contains over 400 columns so I look for a way to encode all desired columns without having to encode them one by one. I use Scikit-learn LabelEncoder to encode the categorical data.

**Converting categorical data into numbers with Pandas and Scikit ,** We load data using Pandas, then convert categorical columns with DictVectorizer from scikit-learn. allows easier manipulation of tabular numeric and non- numeric data. OneHotEncoder takes as input categorical values encoded as To convert some columns from a data frame to a list of dicts, we call� The approach is of viewing the data not on a column level but on a row level. This approach would give the number of distinct values which would automatically distinguish categorical variables from numerical types.

##### Comments

- What have you tried? Have you done any research?
- I tried " df.apply(LabelEncoder().fit_transform) " this works but when I cancat this df frame with another data frame then I'm getting Nan values.
- Better initialize LabelEncoder per class, otherwise you won't be able to perform inverse transformation (that is often needed later when improving or troubleshooting the model).
- You have given clear information but I want to learn deep coding where can I learn?
- Keep the fundamentals clear, get the clarity of the requirement and what kind of model you want to make, study the data carefully, jot down the necessary preprocessing steps and feature scaling steps. Keep practicing
- What if there are 80 columns in a dataset and need to encode, this will take a lot of time, so are there any other ways to encode all the dataset without affecting numerical columns.
- the solution for natheer will do it, but you cannot do inverse transformation then
- What is the use of inverse transformation? and how to perform it.
- Inverse transformation is not relevant for the classification itself, it is only interessting if you want to check after your classification what is the original value...the code is written in my answer already
- Thank you PV8, by the way how to learn all these in detail. I mean machine learning, because I couldn't find the correct resource.