diff --git a/influxdb/_dataframe_client.py b/influxdb/_dataframe_client.py
index ec58cebb..2df2e4ab 100644
--- a/influxdb/_dataframe_client.py
+++ b/influxdb/_dataframe_client.py
@@ -13,7 +13,7 @@
import numpy as np
from .client import InfluxDBClient
-from .line_protocol import _escape_tag
+from .line_protocol import _escape_tag, _to_nanos
def _pandas_time_unit(time_precision):
@@ -267,11 +267,11 @@ def _convert_dataframe_to_json(dataframe,
precision_factor = {
"n": 1,
- "u": 1e3,
- "ms": 1e6,
- "s": 1e9,
- "m": 1e9 * 60,
- "h": 1e9 * 3600,
+ "u": 10 ** 3,
+ "ms": 10 ** 6,
+ "s": 10 ** 9,
+ "m": 10 ** 9 * 60,
+ "h": 10 ** 9 * 3600,
}.get(time_precision, 1)
if not tag_columns:
@@ -279,7 +279,7 @@ def _convert_dataframe_to_json(dataframe,
{'measurement': measurement,
'fields':
rec.replace([np.inf, -np.inf], np.nan).dropna().to_dict(),
- 'time': np.int64(ts.value / precision_factor)}
+ 'time': np.int64(ts.value) // precision_factor}
for ts, (_, rec) in zip(
dataframe.index,
dataframe[field_columns].iterrows()
@@ -293,7 +293,7 @@ def _convert_dataframe_to_json(dataframe,
'tags': dict(list(tag.items()) + list(tags.items())),
'fields':
rec.replace([np.inf, -np.inf], np.nan).dropna().to_dict(),
- 'time': np.int64(ts.value / precision_factor)}
+ 'time': np.int64(ts.value) // precision_factor}
for ts, tag, (_, rec) in zip(
dataframe.index,
dataframe[tag_columns].to_dict('record'),
@@ -354,20 +354,20 @@ def _convert_dataframe_to_lines(self,
precision_factor = {
"n": 1,
- "u": 1e3,
- "ms": 1e6,
- "s": 1e9,
- "m": 1e9 * 60,
- "h": 1e9 * 3600,
+ "u": 10 ** 3,
+ "ms": 10 ** 6,
+ "s": 10 ** 9,
+ "m": 10 ** 9 * 60,
+ "h": 10 ** 9 * 3600,
}.get(time_precision, 1)
# Make array of timestamp ints
if isinstance(dataframe.index, pd.PeriodIndex):
- time = ((dataframe.index.to_timestamp().values.astype(np.int64) /
- precision_factor).astype(np.int64).astype(str))
+ time = ((dataframe.index.to_timestamp().values.astype(np.int64) //
+ precision_factor).astype(str))
else:
- time = ((pd.to_datetime(dataframe.index).values.astype(np.int64) /
- precision_factor).astype(np.int64).astype(str))
+ time = ((pd.to_datetime(dataframe.index).values.astype(np.int64) //
+ precision_factor).astype(str))
# If tag columns exist, make an array of formatted tag keys and values
if tag_columns:
@@ -473,16 +473,13 @@ def _stringify_dataframe(dframe, numeric_precision, datatype='field'):
return dframe
def _datetime_to_epoch(self, datetime, time_precision='s'):
- seconds = (datetime - self.EPOCH).total_seconds()
- if time_precision == 'h':
- return seconds / 3600
- elif time_precision == 'm':
- return seconds / 60
- elif time_precision == 's':
- return seconds
- elif time_precision == 'ms':
- return seconds * 1e3
- elif time_precision == 'u':
- return seconds * 1e6
- elif time_precision == 'n':
- return seconds * 1e9
+ nanos = _to_nanos(datetime)
+ precision_factor = {
+ "n": 1,
+ "u": 10 ** 3,
+ "ms": 10 ** 6,
+ "s": 10 ** 9,
+ "m": 10 ** 9 * 60,
+ "h": 10 ** 9 * 3600,
+ }.get(time_precision, 1)
+ return nanos // precision_factor
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