To select multiple columns, extract and view them thereafter: df is the previously named data frame. Then create a new data frame df1, and select the columns A to D which you want to …

Edit: additionally, the length (in indeces) of a DataFrame based on a subset of columns will be determined by the length of the full file. So if column A has 10 rows, and column B only has 5, …

Select columns by name df.filter(items=['one', 'three']) one three mouse 1 3 rabbit 4 6 Select columns by regular expression df.filter(regex='e$', axis=1) #ending with *e*, for checking …

Jun 12, 2015 · Use .str.startswith on df.columns: import pandas as pd df = pd.DataFrame([[10, 14, 12, 44, 45, 78]], columns=['a', 'b', 'c', 'd1', 'd2', 'd3']) df[df.columns[df.columns.str.startswith('d')]] …

In a similar vein, when reading a file, one may wish to exclude columns upfront, rather than wastefully reading unwanted data into memory and later discarding them. As of pandas 0.20.0, …

pd.DataFrame(df.values[mask], df.index[mask], df.columns).astype(df.dtypes) If the data frame is of mixed type, which our example is, then when we get df.values the resulting array is of dtype …

Apr 21, 2015 · This allows you to drop multiple columns even if you aren't sure they exist, and works for MultiIndex columns too. df.drop(columns=[x for x in ('abc', ('foo', 'bar')) if x in …

Jul 30, 2014 · train.select_dtypes(include=None, exclude=None) train.select_dtypes(include='number') #will include all the numeric types Referred from Jupyter …

Mar 26, 2015 · I want to use a boolean to select the columns with more than 4000 entries from a dataframe comb which has over 1,000 columns. This expression gives me a Boolean …

Oct 6, 2013 · I grouped my dataframe by the two columns below df = pd.DataFrame({'a': [1, 1, 3], 'b': [4.0, 5.5, 6.0], 'c': [7L, 8L, 9L], 'name': ['hello...