Change numpy.NaN to numpy.nan for compatibility with numpy>2.0.0 (#160)

meteostat/interface/normals.py

@@ -163,8 +163,8 @@ def normalize(self):
  else df
  )
 
- # None -> NaN
- temp._data = temp._data.fillna(np.NaN)
+ # None -> nan
+ temp._data = temp._data.fillna(np.nan)
 
  # Return class instance
  return temp

meteostat/interface/timeseries.py

@@ -123,15 +123,15 @@ def _filter_model(self) -> None:
  (pd.isna(self._data[f"{col_name}_flag"]))
  | (self._data[f"{col_name}_flag"].str.contains(self._model_flag)),
  col_name,
- ] = np.NaN
+ ] = np.nan
 
  # Conditionally, remove flags from DataFrame
  if not self._flags:
  self._data.drop(
  map(lambda col_name: f"{col_name}_flag", columns), axis=1, inplace=True
  )
 
- # Drop NaN-only rows
+ # Drop nan-only rows
  self._data.dropna(how="all", subset=columns, inplace=True)
 
  def _init_time_series(

meteostat/series/normalize.py

@@ -9,7 +9,7 @@
 """
 
 from copy import copy
-from numpy import NaN
+from numpy import nan
 import pandas as pd
 import pytz
 from meteostat.core.warn import warn
@@ -56,7 +56,7 @@ def normalize(self):
  # Add columns
  for column in temp._columns[temp._first_met_col :]:
  # Add column to DataFrame
- df[column] = NaN
+ df[column] = nan
 
  result = pd.concat([result, df], axis=0)
 
@@ -70,8 +70,8 @@ def normalize(self):
  .first()
  )
 
- # None -> NaN
- temp._data = temp._data.fillna(NaN)
+ # None -> nan
+ temp._data = temp._data.fillna(nan)
 
  # Return class instance
  return temp

meteostat/units.py

@@ -6,7 +6,7 @@
 The code is licensed under the MIT license.
 """
 
-from numpy import NaN, isnan
+from numpy import nan, isnan
 
 
 def fahrenheit(value):
@@ -62,7 +62,7 @@ def direction(value):
  Convert degrees to wind direction
  """
 
- wdir = NaN
+ wdir = nan
 
  if (337 <= value <= 360) or value <= 23:
  wdir = "N"
@@ -90,7 +90,7 @@ def condition(value):
  """
 
  if isnan(value) or value < 1 or value > 27:
- return NaN
+ return nan
 
  return [
  "Clear",

meteostat/utilities/aggregations.py

@@ -19,7 +19,7 @@ def weighted_average(step: pd.DataFrame) -> pd.DataFrame:
 
  data = np.ma.masked_array(step, np.isnan(step))
  data = np.ma.average(data, axis=0, weights=data[:, -2])
- data = data.filled(np.NaN)
+ data = data.filled(np.nan)
 
  return pd.DataFrame(data=[data], columns=step.columns)
 
@@ -30,7 +30,7 @@ def degree_mean(data: pd.Series) -> float:
  """
 
  if data.isnull().all():
- return np.NaN
+ return np.nan
 
  rads = np.deg2rad(data)
  sums = np.arctan2(np.sum(np.sin(rads)), np.sum(np.cos(rads)))

meteostat/utilities/mutations.py

@@ -52,7 +52,7 @@ def adjust_temp(df: pd.DataFrame, alt: int):
  # Adjust values for all temperature-like data
  for col_name in temp_like:
  if col_name in df.columns:
- df.loc[df[col_name] != np.NaN, col_name] = df[col_name] + (
+ df.loc[df[col_name] != np.nan, col_name] = df[col_name] + (
  temp_diff * ((df["elevation"] - alt) / 100)
  )