Analysis package
Submodules
Analysis.DataframeAnalysis module
- class Analysis.DataframeAnalysis.DataframeAnalysis(root_path, data_path)
Bases:
object- getADF(start_col=None, end_col=None)
Get the ADF of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The ADF result of each column.
- Return type:
dict
- getAverageColumn(start_col=None, end_col=None)
Get the average of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The average value of each column.
- Return type:
pd.DataFrame
- getCorr(method='pearson', start_col=None, end_col=None)
Get the cross correlation of each column in the target dataset from the starting column to the ending column.
- Parameters:
method – The calculation method of cross correlation (‘pearson’ | ‘kendall’ | ‘spearman’)
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The cross correlation of each column.
- Return type:
pd.DataFrame
- getDFGLS(start_col=None, end_col=None)
Get the DF-GLS result of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The DF-GLS result of each column.
- Return type:
dict
- getFFTtopk(col, top_k_seasons=3)
Get the Fast Fourier Transform result of a certain column in the target dataset.
- Parameters:
col – The input column.
top_k_seasons – The number of top k seasons.
- Returns:
The Fast Fourier Transform result of a certain column in the target dataset.
- getInterpolate(start_col=None, end_col=None, **kwargs)
Get the interpolate result of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
kwargs – The arguments of interpolate.
- Returns:
The interpolate result of each column in the target dataset from the starting column to the ending column.
- getKPSS(start_col=None, end_col=None)
Get the KPSS result of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The KPSS result of each column.
- Return type:
dict
- getMaxColumn(start_col=None, end_col=None)
Get the max of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The max of each column.
- Return type:
pd.DataFrame
- getMedianColumn(start_col=None, end_col=None)
Get the median of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The median of each column.
- Return type:
pd.DataFrame
- getMinColumn(start_col=None, end_col=None)
Get the min of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The min of each column.
- Return type:
pd.DataFrame
- getNanIndex(start_col=None, end_col=None)
Get the index containing Nan in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The index containing Nan in the target dataset from the starting column to the ending column.
- Return type:
np.array
- getPhillipsPerron(start_col=None, end_col=None)
Get the phillips perron result of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The phillips perron result of each column.
- Return type:
dict
- getQuantileColumn(percent=[0.25, 0.5, 0.75], start_col=None, end_col=None)
Get the quantile of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
end_col – The ending column.
- Returns:
The quantile of each column.
- Return type:
pd.DataFrame
- getSelfCorr(lag=1, start_col=None, end_col=None)
Get the self correlation of each column in the target dataset from the starting column to the ending column.
- Parameters:
lag – The lagging length used to calculate self correlation.
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The self correlation of each column.
- Return type:
pd.DataFrame
- getShape()
Get the shape of target dataset.
- Returns:
The shape of target dataset.
- getStdColumn(start_col=None, end_col=None)
Get the standard deviation of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The standard deviation value of each column.
- Return type:
pd.DataFrame
- getVarianceColumn(start_col=None, end_col=None)
Get the variance of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The variance value of each column.
- Return type:
pd.DataFrame
- getVarianceRatio(start_col=None, end_col=None)
Get the Variance Ratio result of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The Variance Ratio result of each column.
- Return type:
dict
- getZivotAndrews(start_col=None, end_col=None)
Get the Zivot-Andrews result of each column in the target dataset from the starting column to the ending column.
- Parameters:
start_col (str) – The starting column.
end_col (str) – The ending column.
- Returns:
The Zivot-Andrews result of each column.
- Return type:
dict