- Series.apply(function)
- function’s parameter : multiple individual values (first parameter should be Series’ element itself)
- Elements from Series are calculated by the function
- DataFrame.applymap(function)
- function’s parameter : a single individual value
- Elements from DataFrame are calculated by the function
- DataFrame.apply(function)
- function’s parameter : a Series
- Each column (axis=0) or row (axis=1) is calculated by the function
- DataFrame.pipe(function)
- function’s parameter : a DataFrame
- Dataframe itself is calculated by the function
- Array elements’ dtypes : Timestamp or Period
- Basic structure : year-month-day (ex. 2024-01-02)
- Timestamp : At time point
- form : y-m-d or y-m-d time
- Period : time interval
- form : y or y-m or y-m-d or y-m-d time
- If we set time series (one column) as index of dataframe, it is convenient to deal with these
- only extracting year series or month series or day series
- accessing the specific records using time series elements
in Study / Rosalind on Rosalind problem
A problem from rosalind “Bioinformatics Stronghold” category, String Algorithm
- If data range of a specific column is so wide, it can have influence on the result of machine learning more than other columns.
- We should make the data range from each column be 0 ~ 1 or -1 ~ 1
in Study / Rosalind on Rosalind problem
A problem from rosalind “Bioinformatics Stronghold” category, Probability Calculation