"""Module for reading data.
"""
[docs]def missing_row(dataframe, date_index=True, date_format='%Y/%m/%d'):
"""Return dictionary of missing values dataframe.
Return dictionary contains columns name and list of the index
missing values.
Parameters
----------
dataframe : DataFrame
Dataframe
date_index : bool, optional
Format index to date_format, by default True
date_format : str, optional
String representation of strftime() directive, by default '%Y/%m/%d'
Returns
-------
dict
Return dictionary of columns name and index of missing values.
Examples
--------
Examples for non-date index:
>>> A = pd.DataFrame(data=[[1, 3, 4, np.nan, 2, np.nan],
... [np.nan, 2, 3, np.nan, 1, 4],
... [2, np.nan, 1, 3, 4, np.nan]],
... columns=['A', 'B', 'C', 'D', 'E', 'F'])
... A
A B C D E F
0 1.0 3.0 4 NaN 2 NaN
1 NaN 2.0 3 NaN 1 4.0
2 2.0 NaN 1 3.0 4 NaN
>>> missing_row(A, date_index=False)
{'A': [1], 'B': [2], 'C': [], 'D': [0, 1], 'E': [], 'F': [0, 2]}
Index is timestamp:
>>> date_index = pd.date_range("20190617", "20190619")
>>> A.set_index(date_index, inplace=True)
... A
A B C D E F
2019-06-17 1.0 3.0 4 NaN 2 NaN
2019-06-18 NaN 2.0 3 NaN 1 4.0
2019-06-19 2.0 NaN 1 3.0 4 NaN
>>> missing_row(A, date_format="%m%d")
{'A': ['0618'],
'B': ['0619'],
'C': [],
'D': ['0617', '0618'],
'E': [],
'F': ['0617', '0619']}
"""
missing_data = {}
for column in dataframe.columns:
if date_index:
mask = dataframe[column].isnull()
missing_index = dataframe[mask].index.strftime(date_format)
missing_list = missing_index.values.tolist()
else:
mask = dataframe[column].isnull()
missing_index = dataframe[mask].index
missing_list = missing_index.values.tolist()
missing_data[column] = missing_list
return missing_data
