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Example 1: stackoverflow

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Example 2: stackoverflow

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Example 3: stackoverflow

import os
import pandas as pd
from sklearn.model_selection import train_test_split
import tensorflow as tf
import numpy as np
from sklearn import metrics
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
from keras.preprocessing.text import one_hot

df = pd.read_csv("file.csv")

df.drop('Name', axis=1, inplace=True).

obj_df = df.select_dtypes(include=['object']).copy()
# print(obj_df.head())
obj_df["OUTPUT"] = obj_df["OUTPUT"].astype('category')
obj_df["TYPE_A"] = obj_df["TYPE_A"].astype('category')
obj_df["SIGNAL"] = obj_df["SIGNAL"].astype('category')
obj_df["A-B"] = obj_df["A-B"].astype('category')
# obj_df.dtypes
obj_df["OUTPUT_cat"] = obj_df["OUTPUT"].cat.codes
obj_df["TYPE_A_cat"] = obj_df["TYPE_A"].cat.codes
obj_df["SIGNAL_cat"] = obj_df["SIGNAL"].cat.codes
obj_df["A-B_cat"] = obj_df["A-B"].cat.codes
# print(obj_df.head())
df2 = df[['TRY', 'LOC']]
df3 = obj_df[['OUTPUT_cat', 'TYPE_A_cat', 'SIGNAL_cat', 'A-B_cat']]
df4 = pd.concat([df2, df3], axis=1, sort=False)