Stumbled onto this question while looking for a quick and minimalistic snippet I could use. Had to assemble one myself from posts above. Maybe someone will find it useful: data_frame_trimmed = data_frame.apply(lambda x: x.str.strip() if x.dtype == “object” else x)
Yes, dropna. See http://pandas.pydata.org/pandas-docs/stable/missing_data.html and the DataFrame.dropna docstring: Definition: DataFrame.dropna(self, axis=0, how=’any’, thresh=None, subset=None) Docstring: Return object with labels on given axis omitted where alternately any or all of the data are missing Parameters ———- axis : {0, 1} how : {‘any’, ‘all’} any : if any NA values are present, drop that label all … Read more
Why not just do this? df[‘NewCol’] = df.apply(lambda x: segmentMatch(x[‘TimeCol’], x[‘ResponseCol’]), axis=1) Rather than trying to pass the column as an argument as in your example, we now simply pass the appropriate entries in each row as argument, and store the result in ‘NewCol’.
Use the new dt.dayofweek property: In [2]: df[‘weekday’] = df[‘Timestamp’].dt.dayofweek df Out[2]: Timestamp Value weekday 0 2012-06-01 00:00:00 100 4 1 2012-06-01 00:15:00 150 4 2 2012-06-01 00:30:00 120 4 3 2012-06-01 01:00:00 220 4 4 2012-06-01 01:15:00 80 4 In the situation where the Timestamp is your index you need to reset the index … Read more
As suggested by @StevenG himself, a better answer: df.columns = pd.MultiIndex.from_product([df.columns, [‘C’]]) print(df) # A B # C C # a 0 0 # b 1 1 # c 2 2 # d 3 3 # e 4 4
Simple, Fast, and Pandaic: ngroups Newer versions of the groupby API (pandas >= 0.23) provide this (undocumented) attribute which stores the number of groups in a GroupBy object. # setup df = pd.DataFrame({‘A’: list(‘aabbcccd’)}) dfg = df.groupby(‘A’) # call `.ngroups` on the GroupBy object dfg.ngroups # 4 Note that this is different from GroupBy.groups which … Read more
Try passing columns of the DataFrame directly to matplotlib, as in the examples below, instead of extracting them as numpy arrays. df = pd.DataFrame(np.random.randn(10,2), columns=[‘col1′,’col2’]) df[‘col3’] = np.arange(len(df))**2 * 100 + 100 In [5]: df Out[5]: col1 col2 col3 0 -1.000075 -0.759910 100 1 0.510382 0.972615 200 2 1.872067 -0.731010 500 3 0.131612 1.075142 1000 … Read more
iloc df1 = datasX.iloc[:, :72] df2 = datasX.iloc[:, 72:] (iloc docs)