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How do I select rows from a DataFrame based on column values? Only, when the size of the dataframe approaches million rows, many of the methods tend to take ages when using df[df['col']==val] I wanted to have all possible values of "another_column" that correspond to specific values in "some_column" (in this case in a dictionary)
How can I iterate over rows in a Pandas DataFrame? I have a pandas dataframe, df: c1 c2 0 10 100 1 11 110 2 12 120 How do I iterate over the rows of this dataframe? For every row, I want to access its elements (values in cells) by the n
Selecting multiple columns in a Pandas dataframe - Stack Overflow So your column is returned by df['index'] and the real DataFrame index is returned by df index An Index is a special kind of Series optimized for lookup of its elements' values For df index it's for looking up rows by their label That df columns attribute is also a pd Index array, for looking up columns by their labels
How do I get the row count of a Pandas DataFrame? could use df info () so you get row count (# entries), number of non-null entries in each column, dtypes and memory usage Good complete picture of the df If you're looking for a number you can use programatically then df shape [0]
python - Shuffle DataFrame rows - Stack Overflow Doesn't df = df sample(frac=1) do the exact same thing as df = sklearn utils shuffle(df)? According to my measurements df = df sample(frac=1) is faster and seems to perform the exact same action They also both allocate new memory np random shuffle(df values) is the slowest, but does not allocate new memory
Pandas astype with date (or datetime) - Stack Overflow df = df astype({'date': 'datetime64[ns]'}) worked by the way I think that must have considerable built-in ability for different date formats, year first or last, two or four digit year I just saw 64 ns and thought it wanted the time in nanoseconds
How to get set a pandas index column title or name? To just get the index column names df index names will work for both a single Index or MultiIndex as of the most recent version of pandas As someone who found this while trying to find the best way to get a list of index names + column names, I would have found this answer useful:
How to iterate over columns of a pandas dataframe 66 This answer is to iterate over selected columns as well as all columns in a DF df columns gives a list containing all the columns' names in the DF Now that isn't very helpful if you want to iterate over all the columns But it comes in handy when you want to iterate over columns of your choosing only
python - Insert a row to pandas dataframe - Stack Overflow 289 Just assign row to a particular index, using loc: df loc[-1] = [2, 3, 4] # adding a row df index = df index + 1 # shifting index df = df sort_index() # sorting by index And you get, as desired: A B C 0 2 3 4 1 5 6 7 2 7 8 9 See in Pandas documentation Indexing: Setting with enlargement