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heatwave.py
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heatwave.py
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import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
def adilabad(month):
df=pd.read_csv("Heatwaves\\datafiles\\adilabad.csv")
df['Average of wind_speed_min (Kmph)'].fillna(value=df['Average of wind_speed_min (Kmph)'].mean(), inplace=True)
df['Average of wind_speed_max (Kmph)'].fillna(value=df['Average of wind_speed_max (Kmph)'].mean(), inplace=True)
df1=pd.DataFrame()
df1 = df[df['Row Labels'].str.contains(month, case=False, na=False)]
df1["target"]=df1.shift(-1)["Average of temp_max (â°C)"]
df2=df1.tail(1)
df2.drop(['Row Labels','target'],axis=1,inplace=True)
x=np.array(df2)
df1=df1[df1.target.notnull()]
predictors = df1.columns[~df1.columns.isin(["target", "Row Labels"])]
sc=StandardScaler()
rf = RandomForestRegressor(n_estimators = 300, max_features = 'sqrt', max_depth = 5, random_state = 18)
X=df1[predictors]
y=df1["target"]
X=sc.fit_transform(X)
rf.fit(X,y)
X=sc.fit_transform(X)
y=rf.predict(x)[0]
return y
def karimnagar(month):
df=pd.read_csv("Heatwaves\\datafiles\\karimnagar.csv")
df['Average of wind_speed_min (Kmph)'].fillna(value=df['Average of wind_speed_min (Kmph)'].mean(), inplace=True)
df['Average of wind_speed_max (Kmph)'].fillna(value=df['Average of wind_speed_max (Kmph)'].mean(), inplace=True)
df1=pd.DataFrame()
df1 = df[df['Row Labels'].str.contains(month, case=False, na=False)]
df1["target"]=df1.shift(-1)["Average of temp_max (â°C)"]
df2=df1.tail(1)
df2.drop(['Row Labels','target'],axis=1,inplace=True)
x=np.array(df2)
df1=df1[df1.target.notnull()]
predictors = df1.columns[~df1.columns.isin(["target", "Row Labels"])]
sc=StandardScaler()
rf = RandomForestRegressor(n_estimators = 300, max_features = 'sqrt', max_depth = 5, random_state = 18)
X=df1[predictors]
y=df1["target"]
X=sc.fit_transform(X)
rf.fit(X,y)
X=sc.fit_transform(X)
y=rf.predict(x)[0]
return y
def khammam(month):
df=pd.read_csv("Heatwaves\\datafiles\\khamman.csv")
df['Average of wind_speed_min (Kmph)'].fillna(value=df['Average of wind_speed_min (Kmph)'].mean(), inplace=True)
df['Average of wind_speed_max (Kmph)'].fillna(value=df['Average of wind_speed_max (Kmph)'].mean(), inplace=True)
df1=pd.DataFrame()
df1 = df[df['Row Labels'].str.contains(month, case=False, na=False)]
df1["target"]=df1.shift(-1)["Average of temp_max (â°C)"]
df2=df1.tail(1)
df2.drop(['Row Labels','target'],axis=1,inplace=True)
x=np.array(df2)
df1=df1[df1.target.notnull()]
predictors = df1.columns[~df1.columns.isin(["target", "Row Labels"])]
sc=StandardScaler()
rf = RandomForestRegressor(n_estimators = 300, max_features = 'sqrt', max_depth = 5, random_state = 18)
X=df1[predictors]
y=df1["target"]
X=sc.fit_transform(X)
rf.fit(X,y)
X=sc.fit_transform(X)
y=rf.predict(x)[0]
return y
def Nizamabad(month):
df=pd.read_csv("Heatwaves\\datafiles\\Nizamabad.csv")
df['Average of wind_speed_min (Kmph)'].fillna(value=df['Average of wind_speed_min (Kmph)'].mean(), inplace=True)
df['Average of wind_speed_max (Kmph)'].fillna(value=df['Average of wind_speed_max (Kmph)'].mean(), inplace=True)
df1=pd.DataFrame()
df1 = df[df['Row Labels'].str.contains(month, case=False, na=False)]
df1["target"]=df1.shift(-1)["Average of temp_max (â°C)"]
df2=df1.tail(1)
df2.drop(['Row Labels','target'],axis=1,inplace=True)
x=np.array(df2)
df1=df1[df1.target.notnull()]
predictors = df1.columns[~df1.columns.isin(["target", "Row Labels"])]
sc=StandardScaler()
rf = RandomForestRegressor(n_estimators = 300, max_features = 'sqrt', max_depth = 5, random_state = 18)
X=df1[predictors]
y=df1["target"]
X=sc.fit_transform(X)
rf.fit(X,y)
X=sc.fit_transform(X)
y=rf.predict(x)[0]
return y
def warangal(month):
df=pd.read_csv("Heatwaves\\datafiles\\warangal.csv")
df['Average of wind_speed_min (Kmph)'].fillna(value=df['Average of wind_speed_min (Kmph)'].mean(), inplace=True)
df['Average of wind_speed_max (Kmph)'].fillna(value=df['Average of wind_speed_max (Kmph)'].mean(), inplace=True)
df1=pd.DataFrame()
df1 = df[df['Row Labels'].str.contains(month, case=False, na=False)]
df1["target"]=df1.shift(-1)["Average of temp_max (â°C)"]
df2=df1.tail(1)
df2.drop(['Row Labels','target'],axis=1,inplace=True)
x=np.array(df2)
df1=df1[df1.target.notnull()]
predictors = df1.columns[~df1.columns.isin(["target", "Row Labels"])]
sc=StandardScaler()
rf = RandomForestRegressor(n_estimators = 300, max_features = 'sqrt', max_depth = 5, random_state = 18)
X=df1[predictors]
y=df1["target"]
X=sc.fit_transform(X)
rf.fit(X,y)
X=sc.fit_transform(X)
y=rf.predict(x)[0]
return y