PrettyColorPrinter 0.1

With PrettyColorPrinter, you can print numpy arrays / pandas dataframe / list / dicts / tuple! Shows the path to all items! It even works with nested objects.

With PrettyColorPrinter, you can print numpy arrays / pandas dataframe / list / dicts / tuple! Shows the path to all items! It even works with nested objects.

Very easy to use:

        from PrettyColorPrinter import pqp

        print("Testing")

        df = pd.read_csv(

            "https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv"

        )

        df = df[:40]

        print(

            "Regular Dataframe, take a break of 1 sec every 20 lines, can be pulled by pressing enter, any other key + enter will stop the printing"

        )

        pdp(

            df,

            max_column_size=75,

            repeat_cols=20,

            when_to_take_a_break=20,

            break_how_long=10,

        )

        print("Dataframe as Numpy")

        pdp(df, max_column_size=75, repeat_cols=20, printasnp=True)

        print("Transposed DF as Numpy")

        dftr = df.T

        pdp(dftr, max_column_size=75, repeat_cols=20)

        print("values (pandas)")

        dfvals = df.values

        pdp(dfvals, max_column_size=75, repeat_cols=20)

        print("array np (pandas)")

        dfvarr = df.__array__()

        pdp(dfvarr, max_column_size=75, repeat_cols=20)

        print("dict")

        dfdict = df.to_dict()

        pdp(dfdict, max_column_size=75, repeat_cols=20)

        print("records from df (tuple/list)")

        dfrec = df.to_records()

        pdp(dfrec, max_column_size=75, repeat_cols=20)

        dfrecl = df.to_records().tolist()

        pdp(dfrecl, max_column_size=75, repeat_cols=20)

        dfrect = tuple(df.to_records().tolist())

        pdp(dfrect, max_column_size=25, repeat_cols=20)

        print("pd to numpy")

        dfnp = df.to_numpy()

        pdp(dfnp, max_column_size=25, repeat_cols=20)

        pdp(dfnp.flatten(), reshape_big_1_dim_arrays=10)

        user_dict = {}

        user_dict[12] = {

            "Category 1": {"att_1": 1, "att_2": df.__array__()},

            "Category 2": {"att_1": 23, "att_2": df.to_numpy()},

        }



        pdp(user_dict, repeat_cols=50)