text_plain_output_1.png
import numpy as np

import pandas as pd

Aquifer_Petrignano_input = pd.read_csv('../input/acea-water-prediction/Aquifer_Petrignano.csv')
Lake_Bilancino_input = pd.read_csv('../input/acea-water-prediction/Lake_Bilancino.csv')

Aquifer_Petrignano = Aquifer_Petrignano_input.copy()
Lake_Bilancino = Lake_Bilancino_input.copy()

Aquifer_Petrignano['Date'] = pd.to_datetime(Aquifer_Petrignano['Date'], format='%d/%m/%Y')
Lake_Bilancino['Date'] = pd.to_datetime(Lake_Bilancino['Date'], format='%d/%m/%Y')

pd.concat([pd.Series(Aquifer_Petrignano.isnull().sum()).rename_axis('Aquifer_Petrignano_features/target').to_frame('Missing Value Count').reset_index(), pd.Series(Lake_Bilancino.isnull().sum()).rename_axis('Lake_Bilancino_features/target').to_frame('Missing Value Count').reset_index()], axis=1)

Aquifer_Petrignano.dropna(subset=['Depth_to_Groundwater_P25'], inplace=True)
Lake_Bilancino.dropna(subset=['Flow_Rate'], inplace=True)

Lake_Bilancino.describe()