In this article we will discuss how to change column names or Row Index names in DataFrame object. First of all, create a dataframe object of students records i.e. DataFrame object has an Attribute columns that is basically an Index object and contains column Labels of Dataframe. We can get the ndarray of column names from this Index object i.e. For example let's change the name of column at index 0 i.e.
Good options exist for numeric data but text is a pain. Categorical dtypes are a good option. I need to read and write Pandas DataFrames to disk. Typically we use libraries like pickle to serialize Python objects. For dask.frame we really care about doing this quickly so we're going to also look at a few alternatives.
Do you have benchmarks showing speed improvements over pandas? Pandas can be dreadfully slow, and a restricted implementation that uses only a subset of the "most useful" pandas features might be significantly faster. For instance, consider this benchmark for row and column access to a pandas DataFrame vs a dict of ndarrays columns. For row access, the fastest pandas way to iterate through rows (iterrows) is x6 slower than the simple dict implementation: 24ms vs 4ms. Furthermore, pandas DataFrame a column-based data structure is a whopping 36x slower than a dict of ndarrays for access to a single column of data.
The "default" manner to create a DataFrame from python is to use a list of dictionaries. If you would like to create a DataFrame in a "column oriented" manner, you would use from_dict Using this approach, you get the same results as above. Sometimes it is easier to get your data in a row oriented approach and others in a column oriented. Alternatively you could create your dictionary using python's OrderedDict .