Dimensionality Reduction On Sparse Feature Matrix Preliminaries /* Load libraries */ from sklearn.preprocessing import StandardScaler from sklearn.decomposition import TruncatedSVD from scipy.sparse import csr_matrix from sklearn import datasets import numpy as np Load Digits Data And Make Sparse /* Load the data */ digits = datasets.load_digits() /* Standardize the feature matrix */ X = StandardScaler().fit_transform(digits.data) /* …
Select Date And Time Ranges Preliminaries /* Load library */ import pandas as pd Create pandas Series Time Data /* Create data frame */ df = pd.DataFrame() /* Create datetimes */ df[‘date’] = pd.date_range(‘1/1/2001′, periods=100000, freq=’H’) Select Time Range (Method 1) Use this method if your data frame is not indexed by time. /* Select …
Rolling Time Window Preliminaries import pandas as pd Create Date Data time_index = pd.date_range(’01/01/2010′, periods=5, freq=’M’) df = pd.DataFrame(index=time_index) df[‘Stock_Price’] = [1,2,3,4,5] Create A Rolling Time Window Of Two Rows df.rolling(window=2).mean() Stock_Price 2010-01-31 NaN 2010-02-28 1.5 2010-03-31 2.5 2010-04-30 3.5 2010-05-31 4.5 /* Identify max value in rolling time window */ df.rolling(window=2).max() Stock_Price 2010-01-31 NaN …
Lag A Time Feature Preliminaries import pandas as pd Create Date Data df = pd.DataFrame() df[‘dates’] = pd.date_range(‘1/1/2001′, periods=5, freq=’D’) df[‘stock_price’] = [1.1,2.2,3.3,4.4,5.5] Lag Time Data By One Row df[‘previous_days_stock_price’] = df[‘stock_price’].shift(1) df dates stock_price previous_days_stock_price 0 2001-01-01 1.1 NaN 1 2001-01-02 2.2 1.1 2 2001-01-03 3.3 2.2 3 2001-01-04 4.4 3.3 4 2001-01-05 5.5 …
Handling Missing Values In Time Series Preliminaries import pandas as pd import numpy as np Create Date Data With Gap In Values time_index = pd.date_range(’01/01/2010′, periods=5, freq=’M’) df = pd.DataFrame(index=time_index) df[‘Sales’] = [1.0,2.0,np.nan,np.nan,5.0] Interpolate Missing Values df.interpolate() Sales 2010-01-31 1.0 2010-02-28 2.0 2010-03-31 3.0 2010-04-30 4.0 2010-05-31 5.0 Forward-fill Missing Values df.ffill() Sales 2010-01-31 1.0 …
Encode Days Of The Week Preliminaries import pandas as pd Create Date And Time Data dates = pd.Series(pd.date_range(‘2/2/2002′, periods=3, freq=’M’)) dates 0 2002-02-28 1 2002-03-31 2 2002-04-30 dtype: datetime64[ns] Show Days Of The Week dates.dt.weekday_name 0 Thursday 1 Sunday 2 Tuesday dtype: object Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning …
Calculate Difference Between Dates And Times Preliminaries import pandas as pd Create Date And Time Data df = pd.DataFrame() df[‘Arrived’] = [pd.Timestamp(’01-01-2017′), pd.Timestamp(’01-04-2017′)] df[‘Left’] = [pd.Timestamp(’01-01-2017′), pd.Timestamp(’01-06-2017′)] Calculate Difference (Method 1) df[‘Left’] – df[‘Arrived’] 0 0 days 1 2 days dtype: timedelta64[ns] Calculate Difference (Method 2) pd.Series(delta.days for delta in (df[‘Left’] – df[‘Arrived’])) 0 0 …
Break Up Dates And Times Into Multiple Features Preliminaries import pandas as pd Create Date And Time Data df = pd.DataFrame() df[‘date’] = pd.date_range(‘1/1/2001′, periods=150, freq=’W’) Break Up Dates And Times Into Individual Features df[‘year’] = df[‘date’].dt.year df[‘month’] = df[‘date’].dt.month df[‘day’] = df[‘date’].dt.day df[‘hour’] = df[‘date’].dt.hour df[‘minute’] = df[‘date’].dt.minute df.head(3) date year month day hour …
A step-by-step modelling approach to the forecasting of Bangladesh Population using Box-Jenkins Method In this notebook, the reader will learn how to forecast & predict Bangladesh Population using Box-Jenkins Method in Python. It is a easy step-by-step modelling approach. Python Example for Beginners Special 95% discount 2000+ Applied Machine …
Forecasting & Prediction on Bangladesh Population using Box-Jenkins Method: A step-by-step modelling approach. In this notebook, the reader will learn how to forecast & predict Bangladesh Population using Box-Jenkins Method in Python. It is a easy step-by-step modelling approach. Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & …