I have a tortuous list of column names in a dataframe that I'm reading from an excel sheet. The data is being imported as a multi-indexed dataframe, with two column label levels. I would like to create a list of certain column names that contain a specific string so that I can drop them from the dataframe.
My thought was to use something like this:
# Create list of names for unwanted columns.
lst = [col for col in df.columns if 'ISTD' in col]
# Returns empty.
# Drop columns from dataframe.
df.drop(labels = lst, axis=1, level=0, inplace=True)
The list returns empty though, so I guess the issue is that I don't know how to properly select columns in multi-indexed dataframes. I'm finding it the documentation difficult to understand, so I'm hoping for answers here.
Here are what my column names look like for reference:
df.columns
Out[44]:
MultiIndex([('115 In ( ISTD ) [ He Gas ] ', 'CPS'),
('115 In ( ISTD ) [ He Gas ] ', 'CPS RSD'),
( '137 Ba [ He Gas ] ', 'Conc. RSD'),
( '137 Ba [ He Gas ] ', 'Conc. [ ppb ]'),
( '137 Ba [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
('159 Tb ( ISTD ) [ He Gas ] ', 'CPS'),
('159 Tb ( ISTD ) [ He Gas ] ', 'CPS RSD'),
('175 Lu ( ISTD ) [ He Gas ] ', 'CPS'),
('175 Lu ( ISTD ) [ He Gas ] ', 'CPS RSD'),
( '208 Pb [ He Gas ] ', 'Conc. RSD'),
( '208 Pb [ He Gas ] ', 'Conc. [ ppb ]'),
( '208 Pb [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '23 Na [ He Gas ] ', 'Conc. RSD'),
( '23 Na [ He Gas ] ', 'Conc. [ ppb ]'),
( '23 Na [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '24 Mg [ He Gas ] ', 'Conc. RSD'),
( '24 Mg [ He Gas ] ', 'Conc. [ ppb ]'),
( '24 Mg [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '27 Al [ He Gas ] ', 'Conc. RSD'),
( '27 Al [ He Gas ] ', 'Conc. [ ppb ]'),
( '27 Al [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '39 K [ He Gas ] ', 'Conc. RSD'),
( '39 K [ He Gas ] ', 'Conc. [ ppb ]'),
( '39 K [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '44 Ca [ He Gas ] ', 'Conc. RSD'),
( '44 Ca [ He Gas ] ', 'Conc. [ ppb ]'),
( '44 Ca [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '45 Sc ( ISTD ) [ He Gas ] ', 'CPS'),
( '45 Sc ( ISTD ) [ He Gas ] ', 'CPS RSD'),
( '52 Cr [ He Gas ] ', 'Conc. RSD'),
( '52 Cr [ He Gas ] ', 'Conc. [ ppb ]'),
( '52 Cr [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '55 Mn [ He Gas ] ', 'Conc. RSD'),
( '55 Mn [ He Gas ] ', 'Conc. [ ppb ]'),
( '55 Mn [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '56 Fe [ He Gas ] ', 'Conc. RSD'),
( '56 Fe [ He Gas ] ', 'Conc. [ ppb ]'),
( '56 Fe [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '60 Ni [ He Gas ] ', 'Conc. RSD'),
( '60 Ni [ He Gas ] ', 'Conc. [ ppb ]'),
( '60 Ni [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '63 Cu [ He Gas ] ', 'Conc. RSD'),
( '63 Cu [ He Gas ] ', 'Conc. [ ppb ]'),
( '63 Cu [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '66 Zn [ He Gas ] ', 'Conc. RSD'),
( '66 Zn [ He Gas ] ', 'Conc. [ ppb ]'),
( '66 Zn [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '7 Li ( ISTD ) [ He Gas ] ', 'CPS'),
( '7 Li ( ISTD ) [ He Gas ] ', 'CPS RSD'),
( '72 Ge ( ISTD ) [ He Gas ] ', 'CPS'),
( '72 Ge ( ISTD ) [ He Gas ] ', 'CPS RSD'),
( '75 As [ He Gas ] ', 'Conc. RSD'),
( '75 As [ He Gas ] ', 'Conc. [ ppb ]'),
( '75 As [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '78 Se [ He Gas ] ', 'Conc. RSD'),
( '78 Se [ He Gas ] ', 'Conc. [ ppb ]'),
( '78 Se [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '82 Se [ He Gas ] ', 'Conc. RSD'),
( '82 Se [ He Gas ] ', 'Conc. [ ppb ]'),
( '82 Se [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( '95 Mo [ He Gas ] ', 'Conc. RSD'),
( '95 Mo [ He Gas ] ', 'Conc. [ ppb ]'),
( '95 Mo [ He Gas ] ', 'Meas. Conc. [ ppb ]'),
( 'Sample', 'Acq. Date-Time'),
( 'Sample', 'Comment'),
( 'Sample', 'Data File'),
( 'Sample', 'Level'),
( 'Sample', 'Rjct'),
( 'Sample', 'Sample Name'),
( 'Sample', 'Total Dil.'),
( 'Sample', 'Type'),
( 'Sample', 'Unnamed: 0_level_1'),
( 'Sample', 'Vial Number')]
Thanks for reading.
So, in case of multicolumns, df.columns
returns an object that you can think of as a list of tuples (of type MultiIndex.
You can iterate over them and delete them like this:
cols = [(first, second) for first, second in df.columns if 'ISTD' in second]
df.drop(cols, axis=1, level=1)
This will look for "ISTD" only in the second layer (the second value of the tuples you get from df.columns).
Multi-index columns are a list of tuples. You can do:
lst = [col for col in df.columns if 'ISTD' in col[0]]
df = df.drop(lst, axis=1)
You don't need to create a list, you can not read the columns while reading the file using "usecols"
data = pd.read_excel(directory, usecols = lambda x: False if "unwanted_string" in x else True)
If you still want to make a list, you can get the header row separately, then go through that list to eliminate ones with the unwanted string.
#Read in the column names as a list:
cols = pd.read_excel(directory, header=None, nrows=1, index_col = 0).values[0]
cols = cols.tolist()
#remove the elements that contain the unwanted string
for item in cols:
if "string" in str(item):
cols.remove(item)
else:
continue
#then assign cols list as columns of the dataframe:
data.columns = cols
Here is yet another way. First, create a sample MultiIndex with 4 rows (each row is a tuple):
midx = pd.MultiIndex.from_tuples([
('115 In ( ISTD ) [ He Gas ] ', 'CPS'),
('115 In ( ISTD ) [ He Gas ] ', 'CPS RSD'),
( '137 Ba [ He Gas ] ', 'Conc. RSD'),
( '137 Ba [ He Gas ] ', 'Conc. [ ppb ]'),
])
Now, create the mask (looking for ISTD in the first part of the multi index):
mask = np.array(['ISTD' in idx for idx in midx.get_level_values(0)])
midx[ ~ mask ]
MultiIndex([('137 Ba [ He Gas ] ', 'Conc. RSD'),
('137 Ba [ He Gas ] ', 'Conc. [ ppb ]')],
)
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