def _convert_dataframe_to_lines(self, dataframe, measurement, field_columns=[],
                                tag_columns=[], global_tags={},
                                time_precision=None, numeric_precision=None):

    if not isinstance(dataframe, pd.DataFrame):
        raise TypeError('Must be DataFrame, but type was: {0}.'
                        .format(type(dataframe)))
    if not (isinstance(dataframe.index, pd.tseries.period.PeriodIndex) or
            isinstance(dataframe.index, pd.tseries.index.DatetimeIndex)):
        raise TypeError('Must be DataFrame with DatetimeIndex or \
                        PeriodIndex.')

    string_columns = dataframe.select_dtypes(include=['object']).columns

    if field_columns is None:
        field_columns = []
    if tag_columns is None:
        tag_columns = []

    field_columns = list(field_columns) if list(field_columns) else []
    tag_columns = list(tag_columns) if list(tag_columns) else []

    # Assume that all columns not listed as tag columns are field columns
    if not field_columns:
        field_columns = list(set(dataframe.columns).difference(set(tag_columns)))

    precision_factor = {
        "n": 1,
        "u": 1e3,
        "ms": 1e6,
        "s": 1e9,
        "m": 1e9 * 60,
        "h": 1e9 * 3600,
    }.get(time_precision, 1)


    # Make array of timestamp ints
    time = ((dataframe.index.astype(int) / precision_factor)
            .astype(int).astype(str))

    # If tag columns exist, make an array of formatted tag keys and values
    if tag_columns:
        tag_df = dataframe[tag_columns]
        tag_df = self._stringify_dataframe(tag_df, numeric_precision)
        tags = (',' + ((tag_df.columns + '=').tolist() + tag_df)).sum(axis=1)
        del tag_df

    else:
        tags = ''

    # Make an array of formatted field keys and values
    field_df = dataframe[field_columns]
    field_df = self._stringify_dataframe(field_df, numeric_precision)
    field_df[string_columns] = '"' + field_df[string_columns] + '"'
    field_df = (field_df.columns + '=').tolist() + field_df
    field_df[field_df.columns[1:]] = ',' + field_df[field_df.columns[1:]]
    fields = field_df.sum(axis=1)
    del field_df

    # Add any global tags to formatted tag strings
    if global_tags:
        global_tags = ','.join(['='.join([tag, global_tags[tag]])
                            for tag in global_tags])
        if tag_columns:
            tags = tags + ',' + global_tags
        else:
            tags = ',' + global_tags

    # Generate line protocol string
    points = (measurement +  tags + ' ' +
            fields + ' ' + time).tolist()
    return points

def _stringify_dataframe(self, dataframe, numeric_precision):
    float_columns = dataframe.select_dtypes(include=['floating']).columns
    nonfloat_columns = dataframe.columns[
        ~dataframe.columns.isin(float_columns)
    ]
    numeric_columns = dataframe.select_dtypes(include=['number']).columns

    # Convert dataframe to string
    if numeric_precision is None:
        dataframe = dataframe.astype(str)
    elif numeric_precision == 'full':
        dataframe[float_columns] = dataframe[float_columns].applymap(repr)
        dataframe[nonfloat_columns] = dataframe[nonfloat_columns].astype(str)
    elif isinstance(numeric_precision, int):
        dataframe[numeric_columns] = (dataframe[numeric_columns]
                                    .round(numeric_precision))
        dataframe = dataframe.astype(str)
    else:
        raise ValueError('Invalid numeric precision')
    dataframe.columns = dataframe.columns.astype(str)
    return dataframe

>>> print(df)
                                     color  value  status
2016-08-12 02:17:28.191727141+00:00    red    9.1       1
2016-08-12 02:17:51.082619738+00:00   blue    8.3       0
2016-08-18 07:05:10.979251109+00:00  green    2.0       0
2016-08-18 07:11:11.434227816+00:00  green    1.0       0
2016-08-09 21:48:05.394423800+00:00   blue    1.0       0
2016-08-09 21:48:16.696269144+00:00   blue    1.3       1
2016-08-09 21:48:28.603980937+00:00   blue    0.9       0
2016-08-09 22:01:56.629486728+00:00   blue    0.8       1
2016-08-09 22:02:24.966032304+00:00   blue    0.7       0
2016-08-09 22:16:46.645324098+00:00   blue    1.9       0

>>> _convert_dataframe_to_lines(None, dataframe=df, measurement='var1', tag_columns=['color', 'status'], field_columns=['value'], global_tags={'global':'tag'})

b'var1,color=red,status=1,global=tag value=9.1 1470968248191727104\nvar1,color=blue,status=0,global=tag value=8.3 1470968271082619648\nvar1,color=green,status=0,global=tag value=2.0 1471503910979251200\nvar1,color=green,status=0,global=tag value=1.0 1471504271434227712\nvar1,color=blue,status=0,global=tag value=1.0 1470779285394423808\nvar1,color=blue,status=1,global=tag value=1.3 1470779296696269056\nvar1,color=blue,status=0,global=tag value=0.9 1470779308603981056\nvar1,color=blue,status=1,global=tag value=0.8 1470780116629486848\nvar1,color=blue,status=0,global=tag value=0.7 1470780144966032384\nvar1,color=blue,status=0,global=tag value=1.9 1470781006645324032\n'