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loc is label based indexing so basically looking up a value in a row, iloc is integer row based indexing, ix is a general method that first performs label based, if that fails then it falls to integer based. at is deprecated and it's advised you don't use that anymore.
26 sty 2022 · at[] and iat[] are nearly similar - at[] will return the data from dataframe based on row position/index and column name but iat[] will also return the the data from dataframe based on row index/position and column index/position. loc[] is label based and iloc[] is position based.
.iloc is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. .iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with Python/NumPy slice semantics). Allowed inputs are: An integer e.g. 5.
Adding to the above, Pandas documentation for the at function states: Access a single value for a row/column label pair. Similar to loc, in that both provide label-based lookups. Use at if you only need to get or set a single value in a DataFrame or Series.
8 sie 2023 · You can use loc, iloc, at, and iat to access data in pandas.DataFrame and get/set values. Use square brackets [] as in loc[], not parentheses as in loc(). pandas.DataFrame.loc — pandas 2.0.3 documentation; pandas.DataFrame.iloc — pandas 2.0.3 documentation; pandas.DataFrame.at — pandas 2.0.3 documentation
Series.iat. Access a single value by integer position. Series.loc. Access a group of rows by label (s). Series.iloc. Access a group of rows by integer position (s). Notes. See Fast scalar value getting and setting for more details. Examples.
.iloc [] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. Allowed inputs are: An integer, e.g. 5. A list or array of integers, e.g. [4,3,0]. A slice object with ints, e.g. 1:7. A boolean array.
