df.loc[['B', 'A'], 'X'] B 3 A 1 Name: X, dtype: int64 Notice the dimensionality of the return object when passing arrays. i is an array as it was above, loc returns an object in which an index with those values is returned. In this case, because j was a scalar, loc returned a pd.Series object.

Selecting multiple rows with .loc with a list of strings. df.loc[['Cornelia', 'Jane', 'Dean']] This returns a DataFrame with the rows in the order specified in the list: Selecting multiple rows with .loc with slice notation. Slice notation is defined by a start, stop and step values. When slicing by label, pandas includes the stop value in the ...

@Asclepius df.loc[:, foo] is also giving me SettingWithCopyWarning: asking me to use Try using .loc[row_indexer,col_indexer] = value instead I don't really have any row_indexer since I want to carry out this assignment for all rows.

Nov 1, 2010 · It seems like you need to convert your index to datetime, then use standard indexing / slicing notation.. import pandas as pd, numpy as np df = pd.DataFrame(list(range(365))) # these lines are for demonstration purposes only df['date'] = pd.date_range('2010-1-1', periods=365, freq='D').astype(str) df = df.set_index('date') df.index = pd.to_datetime(df.index) res = df[pd.Timestamp('2010-11-01 ...

Jan 17, 2017 · i want to have 2 conditions in the loc function but the && or and operators dont seem to work.: df: business_id ratings review_text xyz 2 'very bad' xyz 1 '

Note, however, if you slice rows with loc, instead of iloc, you'll get rows 1, 2 and 3 assuming you have a RangeIndex. See details here.) However, [] does not work in the following situations: You can select a single row with df.loc[row_label] You can …

.loc of a data frame selects all the elements located by indexed_rows and labeled_columns as given in its argument. Instead, .at selects particular element of a data frame positioned at the given indexed_row and labeled_column. Also, .at takes one row and one column as input argument, whereas .loc may take multiple rows

May 21, 2019 · .loc on multiple columns with the same condition Hot Network Questions Why does the LaTeX kernel define 'purify' equivalents for text font macros e.g. \textbf or \itshape?

Thus, df[boolean_mask] does not always behave the same as df.loc[boolean_mask]. Even though this is arguably an unlikely use case, I would recommend always using df.loc[boolean_mask] instead of df[boolean_mask] because the meaning of df.loc's syntax is explicit. With df.loc[indexer] you know automatically that df.loc is selecting rows.

Feb 27, 2019 · Also, while where is only for conditional filtering, loc is the standard way of selecting in Pandas, along with iloc. loc uses row and column names, while iloc uses their index number. So with loc you could choose to return, say, df.loc[0:1, ['Gender', 'Goals']]: Gender Goals 0 …