The column names (which are strings) cannot be sliced in the manner you tried. Here you have a couple of options. If you know from context which variables you want to slice out, you can just return a view of only those columns by passing a list into the __getitem__ syntax (the []’s). Alternatively, if … Read more
To delimit by a tab you can use the sep argument of to_csv: To use a specific encoding (e.g. ‘utf-8’) use the encoding argument:
DataFrame.iterrows is a generator which yields both the index and row (as a Series):
DataFrame.iterrows is a generator which yields both the index and row (as a Series):
The error message says that if you’re passing scalar values, you have to pass an index. So you can either not use scalar values for the columns — e.g. use a list: or use scalar values and pass an index:
The SettingWithCopyWarning was created to flag potentially confusing “chained” assignments, such as the following, which does not always work as expected, particularly when the first selection returns a copy. [see GH5390 and GH5597 for background discussion.] The warning offers a suggestion to rewrite as follows: However, this doesn’t fit your usage, which is equivalent to: While it’s clear that you don’t care … Read more
The SettingWithCopyWarning was created to flag potentially confusing “chained” assignments, such as the following, which does not always work as expected, particularly when the first selection returns a copy. [see GH5390 and GH5597 for background discussion.] The warning offers a suggestion to rewrite as follows: However, this doesn’t fit your usage, which is equivalent to: While it’s clear that you don’t care … Read more
As you’ve guessed, the right syntax is It’s difficult to make del df.column_name work simply as the result of syntactic limitations in Python. del df[name] gets translated to df.__delitem__(name) under the covers by Python.
The code you’ve posted already does what you want, but does not do it “in place.” Try adding inplace=True to reset_index() or else reassigning the result to df_all. Note that you can also use inplace=True with dropna(), so: Does it all in place. Or, to reassign df_all.
DataFrame.reset_index is what you’re looking for. If you don’t want it saved as a column, then do: If you don’t want to reassign: