Open In Colab

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns

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

df.head()
date meantemp humidity wind_speed meanpressure
0 2013-01-01 10.000000 84.500000 0.000000 1015.666667
1 2013-01-02 7.400000 92.000000 2.980000 1017.800000
2 2013-01-03 7.166667 87.000000 4.633333 1018.666667
3 2013-01-04 8.666667 71.333333 1.233333 1017.166667
4 2013-01-05 6.000000 86.833333 3.700000 1016.500000 
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1462 entries, 0 to 1461
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   date          1462 non-null   object 
 1   meantemp      1462 non-null   float64
 2   humidity      1462 non-null   float64
 3   wind_speed    1462 non-null   float64
 4   meanpressure  1462 non-null   float64
dtypes: float64(4), object(1)
memory usage: 57.2+ KB

df.isna().sum()

date            0
meantemp        0
humidity        0
wind_speed      0
meanpressure    0
dtype: int64
df.date = pd.DatetimeIndex(df.date.values)
df = df.set_index('date')
df.head()
meantemp humidity wind_speed meanpressure
date
2013-01-01 10.000000 84.500000 0.000000 1015.666667
2013-01-02 7.400000 92.000000 2.980000 1017.800000
2013-01-03 7.166667 87.000000 4.633333 1018.666667
2013-01-04 8.666667 71.333333 1.233333 1017.166667
2013-01-05 6.000000 86.833333 3.700000 1016.500000 
df.describe()
meantemp humidity wind_speed meanpressure
count 1462.000000 1462.000000 1462.000000 1462.000000
mean 25.495521 60.771702 6.802209 1011.104548
std 7.348103 16.769652 4.561602 180.231668
min 6.000000 13.428571 0.000000 -3.041667
25% 18.857143 50.375000 3.475000 1001.580357
50% 27.714286 62.625000 6.221667 1008.563492
75% 31.305804 72.218750 9.238235 1014.944901
max 38.714286 100.000000 42.220000 7679.333333

Existem outliers, segundo boxplot anteriores do EDA, em:

  • humidade
  • pressão atmosférica
  • velocidade do vento
def remove_outliers(df, col):
  q1 = df[col].quantile(0.25)
  q3 = df[col].quantile(0.75)
  iqr = q3-q1
  df = df[(df[col]>(q1-1.5*iqr)) & (df[col]<(q3+1.5*iqr))]
  return df

for col in ["wind_speed","meanpressure"]:
  df=remove_outliers(df, col)

df[['meanpressure']].boxplot()
plt.show()

selected_features = ['humidity', 'wind_speed']
target = ['meantemp']
df = df[selected_features + target]

train = df[df.index<"2016-08-01"] 
test = df[df.index>="2016-08-01"]

train['month'] = train.index.month
train = train.join(train.groupby('month').agg({'meantemp':'mean'}).reset_index(), on='month', rsuffix='_month')

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.

test['month'] = test.index.month
test = test.join(train.groupby('month').agg({'meantemp':'mean'}).reset_index(), on='month', rsuffix='_month')

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.

train.drop(columns=['month','month_month'], inplace=True)
test.drop(columns=['month','month_month'], inplace=True)

from sklearn.preprocessing import MinMaxScaler

min_max = MinMaxScaler()

train = pd.DataFrame(data = min_max.fit_transform(train), columns=train.columns)

test = pd.DataFrame(data =min_max.transform(test), columns=test.columns)

train.head()
humidity wind_speed meantemp meantemp_month
0 0.840372 0.000000 0.122271 0.140201
1 0.929054 0.173888 0.042795 0.140201
2 0.869932 0.270362 0.035662 0.140201
3 0.684685 0.071967 0.081514 0.140201
4 0.867962 0.215901 0.000000 0.140201 
def remove_outliers(df, col):
  q1 = df[col].quantile(0,25)
  q3 = df[col].quantile(0,75)
  iqr = q3-q1
  df[(df[col]>(q1-1.5*iqr)) & (df[col]<(q3+1.5*iqr))]
  return df