<jupyter_start><jupyter_text>Extracting Time Series[▲ Overview](0.0-Overview.ipynb)[◀ Loading and Decoding Dataset](2.0-Loading-dataset.ipynb)[▶ Data Exploration](3.0-Exploring-timeseries.ipynb)<jupyter_code>import pandas as pd
from australian_housing import paths
from australian_housing.data.extract_timeseries import new_south_wales_index
df = pd.read_csv(paths.manager.interim_data_file, index_col=0)
df.head()
for head in ('Measure', 'Sector of Ownership', 'Type of work', 'Type of building', 'Geography Level', 'Region', 'Frequency'):
    df[head] = df[head].astype('category')
df.describe(include='category')<jupyter_output><empty_output><jupyter_text>In this dataset `Measure`, `Sector of Ownership`, `Type of work` and `Frequency` do not contain further information. For `Type of building`, `Geography Level`, and `Region` we need to select the correct values requested in the exercise (`Type of building` = `Houses`, `Geography Level` = `States and Territories`, and `Region` = `New South Wales`).<jupyter_code>df['Measure'].unique()
df['Sector of Ownership'].unique()
df['Type of work'].unique()
df['Type of building'].unique()
df['Geography Level'].unique()
df['Region'].unique()
nsw = df[new_south_wales_index(df)]
nsw
nsw[['Value']].head()<jupyter_output><empty_output>