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Is there a more sophisticated way to check if a dataframe df contains 2 columns named Column 1 and Column 2:
if numpy.all(map(lambda c: c in df.columns, ['Column 1', 'Columns 2'])):
do_something()
862
Use the COL_LENGTH system function! You basically just pass the name of the table your interested in, and the name of the column within that table you want to check. This function returns the defined length of the column in bytes, if that column exists. If the column does not exist, the function returns NULL.
To select multiple columns from a table, simply separate the column names with commas! For example, this query selects two columns, name and birthdate , from the people table: SELECT name, birthdate FROM people; Sometimes, you may want to select all columns from a table.
Checking Existence of the Column: For checking the existence we need to use the COL_LENGTH() function. COL_LENGTH() function returns the defined length of a column in bytes. This function can be used with the IF ELSE condition to check if the column exists or not.
You can use Index.isin:
df = pd.DataFrame({'A':[1,2,3],
'B':[4,5,6],
'C':[7,8,9],
'D':[1,3,5],
'E':[5,3,6],
'F':[7,4,3]})
print (df)
A B C D E F
0 1 4 7 1 5 7
1 2 5 8 3 3 4
2 3 6 9 5 6 3
If need check at least one value use any
cols = ['A', 'B']
print (df.columns.isin(cols).any())
True
cols = ['W', 'B']
print (df.columns.isin(cols).any())
True
cols = ['W', 'Z']
print (df.columns.isin(cols).any())
False
If need check all values:
cols = ['A', 'B', 'C','D','E','F']
print (df.columns.isin(cols).all())
True
cols = ['W', 'Z']
print (df.columns.isin(cols).all())
False
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