from google.colab import drive
drive.mount('/content/drive/')

# ご自身の環境に合わせてください
%cd /content/drive/My\ Drive/予測コンペ/ProbSpace/不動産取引価格予測
!ls

import featuretools as ft
import lightgbm as lgb
#import optuna
import numpy as np
import sklearn.datasets
import sklearn.metrics
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
import xgboost as xgb
import seaborn as sns
from tensorflow import keras
import keras.layers as L
from keras.models import Model
from sklearn.decomposition import PCA
from keras import losses
from sklearn import preprocessing
from sklearn.metrics import log_loss
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

!pip install japanize-matplotlib
import japanize_matplotlib
!pip install jeraconv
from jeraconv import jeraconv

train = pd.read_csv("train_data.csv")
print("train shape is " + str(train.shape))
train.head()

test = pd.read_csv("test_data.csv")
iddf = test[["id"]]
print("test shape is " + str(test.shape))
test.head()

for i, val in enumerate(train.columns):
  print("====================")
  print(i, val)
  if val == "id":
    continue
  train[val].value_counts(dropna=False).plot.bar(figsize=(19, 3), fontsize=18, title=val)
  plt.show()