Motivation

pandas is a great tool for representing structured data in python, similar to Table in matlab and dataframe in R .

Using it to interact with table data (like experimental records) on hard drive and process it in python is also great!

Create Dataframe

Source:

• List of tuples
• np.ndarray
• dictionary

Read / Write

• read_excel

• read_csv

• to_excel

• to_csv

Data Structure

• pandas array
  • Can be transformed into numpy by .to_numpy() . Or to normal list by .to_list()

Interaction

• Slicing using boolean array. and df.index is the normal row number starting from 0.
  • ExpTable[ExpTable.index == 100]
  • Selecting rows using condition
    • color_and_shape = df.loc[(df.Color == 'Green') & (df.Shape == 'Rectangle')]
  • ExpTable.index[ExpTable.comments.str.contains("Evolution")]
  • Caveat: you cannot use np type array to directly slice a dataFrame. But you can use it to slice the individual columns.
• Filtering using sub-string pattern
  • ExpTable.Expi[ExpTable.comments.str.contains("Evolution")]
  • Or df.ephysFN[df.ephysFN.str.contains("Alfa")==True]
• Remove lines containing any nan or all nan
  • df = df.dropna(axis=0, how='all')
  • df = df.dropna(axis=0, how='any')
  • Note: dropna will not move the index of the original array! Thus the index of the original lines will not be changed. However there will be gap in index space. reindex will close the gap!
    • df = df.reindex(index=range(df.shape[0]))
• Fill nan with value
  • df.comments.fillna(value="", inplace=True)
  • Note: just like numpy, df function usually returns a new view or object instead of write inplace! Unless you specify that!
• Concatenate 2 tables together
  • pd.concat([df_old,df_new], axis=0, ignore_index=True)