use PRIMARY KEY and UNIQUE in CSV
For a very limited situation such that you don't need performance and want to use CSV as database.
from csvkey import Connection import pandas as pd import numpy as np # prepare DataFrame data = pd.DataFrame() data['A'] = pd.Series([1,2,3], dtype='int') data['B'] = pd.Series([4,5,6], dtype='float32') data['C'] = pd.Series([7,8,9], dtype='float64') # register database conn = Connection() conn.initialize(data, r'C:\TEST\database.csv', primary=['A', 'B'], unique=['C'], notnull=['C']) # database.csv and the configuration file (default: csv.conf) are generated in C:\TEST\ # set 'A' and 'B' columns as a primary key # values in 'C' column must be unique and not NaN # connect to database.csv conn.connect(r'C:\TEST\database.csv') conn.df.dtypes # dtypes are preserved # change values in conn.df conn.df.loc[0, 'C'] = 8 conn.commit() # raise ValueError because 8 is not unique conn.df.loc[0, 'C'] = np.nan conn.commit() # raise ValueError because NaN is not allowed in 'C' conn.df.loc[0, 'C'] = -1 conn.commit() # OK conn.df.loc[2, ['A', 'B']] = [1, 4] conn.commit() # raise ValueError because primary keys are duplicated
pip install csvkey
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