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"""
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2
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@file
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3
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@brief Fast data manipulations.
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4
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"""
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5
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4
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import pandas
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6
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7
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8
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4
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def df2array(df, check=True):
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9
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"""
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10
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Converts a dataframe into a :epkg:`numpy:array`
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11
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without copying. :epkg:`pandas` is merging
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consecutive columns sharing the same type
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13
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into one memory block. The function can be used
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14
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only if the data is stored in one block and one type
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15
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as a consequence.
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16
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@param df dataframe
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@param check verifies the operation can be done (True)
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or skip verification (False)
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@return :epkg:`numpy:array`
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See `data member <https://pandas.pydata.org/pandas-docs/stable/search.html?q=pointer&check_keywords=yes&area=default>`_,
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`_data <https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L322>`_.
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25
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.. seealso:: @see fn df2array
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"""
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4
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if check:
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28
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4
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if not isinstance(df, pandas.DataFrame):
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raise TypeError("df is not a pandas.DataFrame") # pragma: no cover
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30
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4
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if len(df._data.blocks) != 1:
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31
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4
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raise ValueError(
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32
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"The dataframe has many block of data. There should be only one column type.")
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33
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4
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return df._data.blocks[0].values
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34
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35
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36
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4
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def df2arrays(df, sep=",", check=True):
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37
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"""
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Converts a dataframe into a list of
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a list of tuple *(column name, :epkg:`numpy:array`)*
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without copying. :epkg:`pandas` is merging
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consecutive columns sharing the same type
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into one memory block. That's what the function extracts
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43
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44
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@param df dataframe
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@param check verifies the operation can be done (True)
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46
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or skip verification (False)
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@param sep columns separator
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@return a list of tuple ``(column, array)``
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49
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50
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Example:
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51
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52
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.. runpython::
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53
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:showcode:
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54
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55
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from pandas import DataFrame
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56
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from cpyquickhelper.fastdata import df2arrays
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57
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58
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df = DataFrame([dict(a=3.4, b=5.6, c="e"),
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59
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dict(a=3.5, b=5.7, c="r")])
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60
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arr = df2arrays(df)
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print(arr)
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62
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63
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64
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.. seealso:: @see fn df2array
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65
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"""
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66
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4
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if check:
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67
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4
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if not isinstance(df, pandas.DataFrame):
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68
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raise TypeError("df is not a pandas.DataFrame") # pragma: no cover
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69
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70
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4
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cols = df.columns
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71
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4
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res = []
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72
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4
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pos = 0
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73
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4
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for b in df._data.blocks:
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74
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4
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name = sep.join(cols[pos:pos + b.shape[1]])
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75
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4
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res.append((name, b.values))
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76
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4
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pos += b.shape[1]
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77
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4
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return res
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sdpython