diff --git a/examples/daily/aggregate_regional.py b/examples/daily/aggregate_regional.py
index af6080f..e842311 100644
--- a/examples/daily/aggregate_regional.py
+++ b/examples/daily/aggregate_regional.py
@@ -8,32 +8,34 @@
 The code is licensed under the MIT license.
 """
 
-from datetime import datetime
-import matplotlib.pyplot as plt
-from meteostat import Stations, Daily
+if __name__ == '__main__':
 
-# Configuration
-Daily.max_threads = 5
+    from datetime import datetime
+    import matplotlib.pyplot as plt
+    from meteostat import Stations, Daily
 
-# Time period
-start = datetime(1980, 1, 1)
-end = datetime(2019, 12, 31)
+    # Configuration
+    Daily.cores = 12
 
-# Get random weather stations in the US
-stations = Stations()
-stations = stations.region('US')
-stations = stations.inventory('daily', (start, end))
-stations = stations.fetch(limit=20, sample=True)
+    # Time period
+    start = datetime(1980, 1, 1)
+    end = datetime(2019, 12, 31)
 
-# Get daily data
-data = Daily(stations, start, end)
+    # Get random weather stations in the US
+    stations = Stations()
+    stations = stations.region('US')
+    stations = stations.inventory('daily', (start, end))
+    stations = stations.fetch(limit=50, sample=True)
 
-# Normalize & aggregate
-data = data.normalize().aggregate('1Y', spatial=True).fetch()
+    # Get daily data
+    data = Daily(stations, start, end)
 
-# Chart title
-TITLE = 'Average US Annual Temperature from 1980 to 2019'
+    # Normalize & aggregate
+    data = data.normalize().aggregate('1Y', spatial=True).fetch()
 
-# Plot chart
-data.plot(y=['tavg'], title=TITLE)
-plt.show()
+    # Chart title
+    TITLE = 'Average US Annual Temperature from 1980 to 2019'
+
+    # Plot chart
+    data.plot(y=['tavg'], title=TITLE)
+    plt.show()
diff --git a/examples/hourly/performance.py b/examples/hourly/performance.py
new file mode 100644
index 0000000..a04191d
--- /dev/null
+++ b/examples/hourly/performance.py
@@ -0,0 +1,34 @@
+"""
+Example: Hourly point data performance
+
+Meteorological data provided by Meteostat (https://dev.meteostat.net)
+under the terms of the Creative Commons Attribution-NonCommercial
+4.0 International Public License.
+
+The code is licensed under the MIT license.
+"""
+
+if __name__ == '__main__':
+
+    from timeit import default_timer as timer
+
+    # Get start time
+    s = timer()
+
+    # Run script
+    from datetime import datetime
+    from meteostat import Hourly
+
+    Hourly.cores = 12
+
+    start = datetime(1960, 1, 1)
+    end = datetime(2021, 1, 1, 23, 59)
+
+    data = Hourly('10637', start, end, timezone='Europe/Berlin')
+    data = data.fetch()
+
+    # Get end time
+    e = timer()
+
+    # Print performance
+    print(e - s)
diff --git a/meteostat/__init__.py b/meteostat/__init__.py
index 27f4a17..566681d 100644
--- a/meteostat/__init__.py
+++ b/meteostat/__init__.py
@@ -12,8 +12,10 @@
 """
 
 __appname__ = 'meteostat'
-__version__ = '1.4.6'
+__version__ = '1.5.0'
 
+from .interface.base import Base
+from .interface.timeseries import Timeseries
 from .interface.stations import Stations
 from .interface.point import Point
 from .interface.hourly import Hourly
diff --git a/meteostat/core/cache.py b/meteostat/core/cache.py
index f0cf59a..3b843ff 100644
--- a/meteostat/core/cache.py
+++ b/meteostat/core/cache.py
@@ -42,7 +42,10 @@ def file_in_cache(
 
     # Make sure the cache directory exists
     if not os.path.exists(directory):
-        os.makedirs(directory)
+        try:
+            os.makedirs(directory)
+        except FileExistsError:
+            pass
 
     # Return the file path if it exists
     if os.path.isfile(path) and time.time() - \
diff --git a/meteostat/core/config.py b/meteostat/core/config.py
deleted file mode 100644
index 5c0b8cb..0000000
--- a/meteostat/core/config.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""
-Core Class - Configuration
-
-Meteorological data provided by Meteostat (https://dev.meteostat.net)
-under the terms of the Creative Commons Attribution-NonCommercial
-4.0 International Public License.
-
-The code is licensed under the MIT license.
-"""
-
-import os
-
-# Base URL of the Meteostat bulk data interface
-endpoint: str = 'https://bulk.meteostat.net/v2/'
-
-# Location of the cache directory
-cache_dir: str = os.path.expanduser(
-    '~') + os.sep + '.meteostat' + os.sep + 'cache'
-
-# Maximum age of a cached file in seconds
-max_age: int = 24 * 60 * 60
-
-# Maximum number of threads used for downloading files
-max_threads: int = 1
diff --git a/meteostat/core/loader.py b/meteostat/core/loader.py
index 9a179c0..efb1441 100644
--- a/meteostat/core/loader.py
+++ b/meteostat/core/loader.py
@@ -9,6 +9,7 @@
 """
 
 from urllib.error import HTTPError
+from multiprocessing import Pool
 from multiprocessing.pool import ThreadPool
 from typing import Callable
 import pandas as pd
@@ -18,27 +19,49 @@
 def processing_handler(
     datasets: list,
     load: Callable[[dict], None],
-    max_threads: int
+    cores: int,
+    threads: int
 ) -> None:
     """
-    Load multiple datasets simultaneously
+    Load multiple datasets (simultaneously)
     """
 
-    # Single-thread processing
-    if max_threads < 2:
+    # Data output
+    output = []
 
-        for dataset in datasets:
-            load(*dataset)
+    # Multi-core processing
+    if cores > 1 and len(datasets) > 1:
+
+        # Create process pool
+        with Pool(cores) as pool:
+
+            # Process datasets in pool
+            output = pool.starmap(load, datasets)
+
+            # Wait for Pool to finish
+            pool.close()
+            pool.join()
 
     # Multi-thread processing
+    elif threads > 1 and len(datasets) > 1:
+
+        # Create process pool
+        with ThreadPool(cores) as pool:
+
+            # Process datasets in pool
+            output = pool.starmap(load, datasets)
+
+            # Wait for Pool to finish
+            pool.close()
+            pool.join()
+
+    # Single-thread processing
     else:
 
-        pool = ThreadPool(max_threads)
-        pool.starmap(load, datasets)
+        for dataset in datasets:
+            output.append(load(*dataset))
 
-        # Wait for Pool to finish
-        pool.close()
-        pool.join()
+    return pd.concat(output)
 
 
 def load_handler(
diff --git a/meteostat/interface/base.py b/meteostat/interface/base.py
index d258175..35e6461 100644
--- a/meteostat/interface/base.py
+++ b/meteostat/interface/base.py
@@ -8,6 +8,8 @@
 The code is licensed under the MIT license.
 """
 
+import os
+
 
 class Base:
 
@@ -15,5 +17,18 @@ class Base:
     Base class that provides features which are used across the package
     """
 
-    # Import configuration
-    from meteostat.core.config import endpoint, cache_dir, max_age, max_threads
+    # Base URL of the Meteostat bulk data interface
+    endpoint: str = 'https://bulk.meteostat.net/v2/'
+
+    # Location of the cache directory
+    cache_dir: str = os.path.expanduser(
+        '~') + os.sep + '.meteostat' + os.sep + 'cache'
+
+    # Maximum age of a cached file in seconds
+    max_age: int = 24 * 60 * 60
+
+    # Number of cores used for processing files
+    cores: int = 1
+
+    # Number of threads used for processing files
+    threads: int = 1
diff --git a/meteostat/interface/daily.py b/meteostat/interface/daily.py
index feebbbe..4e21b0b 100644
--- a/meteostat/interface/daily.py
+++ b/meteostat/interface/daily.py
@@ -16,11 +16,11 @@
 from meteostat.core.loader import processing_handler, load_handler
 from meteostat.utilities.validations import validate_series
 from meteostat.utilities.aggregations import degree_mean, weighted_average
-from meteostat.interface.base import Base
+from meteostat.interface.timeseries import Timeseries
 from meteostat.interface.point import Point
 
 
-class Daily(Base):
+class Daily(Timeseries):
 
     """
     Retrieve daily weather observations for one or multiple weather stations or
@@ -30,21 +30,6 @@ class Daily(Base):
     # The cache subdirectory
     cache_subdir: str = 'daily'
 
-    # The list of weather Stations
-    _stations: pd.Index = None
-
-    # The start date
-    _start: datetime = None
-
-    # The end date
-    _end: datetime = None
-
-    # Include model data?
-    _model: bool = True
-
-    # The data frame
-    _data: pd.DataFrame = pd.DataFrame()
-
     # Default frequency
     _freq: str = '1D'
 
@@ -143,14 +128,11 @@ def _load(
             # Get time index
             time = df.index.get_level_values('time')
 
-            # Filter & append
-            self._data = self._data.append(
-                df.loc[(time >= self._start) & (time <= self._end)])
+            # Filter & return
+            return df.loc[(time >= self._start) & (time <= self._end)]
 
-        else:
-
-            # Append
-            self._data = self._data.append(df)
+        # Return
+        return df
 
     def _get_data(self) -> None:
         """
@@ -168,12 +150,11 @@ def _get_data(self) -> None:
                 ))
 
             # Data Processing
-            processing_handler(datasets, self._load, self.max_threads)
+            return processing_handler(
+                datasets, self._load, self.cores, self.threads)
 
-        else:
-
-            # Empty DataFrame
-            self._data = pd.DataFrame(columns=[*self._types])
+        # Empty DataFrame
+        return pd.DataFrame(columns=[*self._types])
 
     def _resolve_point(
         self,
@@ -263,7 +244,7 @@ def __init__(
         self._model = model
 
         # Get data for all weather stations
-        self._get_data()
+        self._data = self._get_data()
 
         # Interpolate data
         if isinstance(loc, Point):
@@ -279,14 +260,3 @@ def expected_rows(self) -> int:
         """
 
         return (self._end - self._start).days + 1
-
-    # Import methods
-    from meteostat.series.normalize import normalize
-    from meteostat.series.interpolate import interpolate
-    from meteostat.series.aggregate import aggregate
-    from meteostat.series.convert import convert
-    from meteostat.series.coverage import coverage
-    from meteostat.series.count import count
-    from meteostat.series.fetch import fetch
-    from meteostat.series.stations import stations
-    from meteostat.core.cache import clear_cache
diff --git a/meteostat/interface/hourly.py b/meteostat/interface/hourly.py
index 11f56b6..6f918df 100644
--- a/meteostat/interface/hourly.py
+++ b/meteostat/interface/hourly.py
@@ -18,11 +18,11 @@
 from meteostat.core.loader import processing_handler, load_handler
 from meteostat.utilities.validations import validate_series
 from meteostat.utilities.aggregations import degree_mean, weighted_average
-from meteostat.interface.base import Base
+from meteostat.interface.timeseries import Timeseries
 from meteostat.interface.point import Point
 
 
-class Hourly(Base):
+class Hourly(Timeseries):
 
     """
     Retrieve hourly weather observations for one or multiple weather stations or
@@ -35,24 +35,9 @@ class Hourly(Base):
     # Specify if the library should use chunks or full dumps
     chunked: bool = True
 
-    # The list of weather Stations
-    _stations: pd.Index = None
-
-    # The start date
-    _start: datetime = None
-
-    # The end date
-    _end: datetime = None
-
     # The time zone
     _timezone: str = None
 
-    # Include model data?
-    _model: bool = True
-
-    # The data frame
-    _data: pd.DataFrame = pd.DataFrame()
-
     # Default frequency
     _freq: str = '1H'
 
@@ -200,14 +185,11 @@ def _load(
             # Get time index
             time = df.index.get_level_values('time')
 
-            # Filter & append
-            self._data = self._data.append(
-                df.loc[(time >= self._start) & (time <= self._end)])
+            # Filter & return
+            return df.loc[(time >= self._start) & (time <= self._end)]
 
-        else:
-
-            # Append
-            self._data = self._data.append(df)
+        # Return
+        return df
 
     def _get_data(self) -> None:
         """
@@ -237,12 +219,10 @@ def _get_data(self) -> None:
                     ))
 
             # Data Processing
-            processing_handler(datasets, self._load, self.max_threads)
-
-        else:
+            return processing_handler(
+                datasets, self._load, self.cores, self.threads)
 
-            # Empty DataFrame
-            self._data = pd.DataFrame(columns=[*self._types])
+        return pd.DataFrame(columns=[*self._types])
 
     def _resolve_point(
         self,
@@ -260,8 +240,8 @@ def _resolve_point(
 
         if method == 'nearest':
 
-            self._data = self._data.groupby(
-                pd.Grouper(level='time', freq=self._freq)).agg('first')
+            self._data = self._data.groupby(pd.Grouper(
+                level='time', freq=self._freq)).agg('first')
 
         else:
 
@@ -328,7 +308,7 @@ def __init__(
         self._model = model
 
         # Get data for all weather stations
-        self._get_data()
+        self._data = self._get_data()
 
         # Interpolate data
         if isinstance(loc, Point):
@@ -344,14 +324,3 @@ def expected_rows(self) -> int:
         """
 
         return floor((self._end - self._start).total_seconds() / 3600) + 1
-
-    # Import methods
-    from meteostat.series.normalize import normalize
-    from meteostat.series.interpolate import interpolate
-    from meteostat.series.aggregate import aggregate
-    from meteostat.series.convert import convert
-    from meteostat.series.coverage import coverage
-    from meteostat.series.count import count
-    from meteostat.series.fetch import fetch
-    from meteostat.series.stations import stations
-    from meteostat.core.cache import clear_cache
diff --git a/meteostat/interface/monthly.py b/meteostat/interface/monthly.py
index 47593ce..57774d4 100644
--- a/meteostat/interface/monthly.py
+++ b/meteostat/interface/monthly.py
@@ -16,11 +16,11 @@
 from meteostat.core.loader import processing_handler, load_handler
 from meteostat.utilities.validations import validate_series
 from meteostat.utilities.aggregations import degree_mean, weighted_average
-from meteostat.interface.base import Base
+from meteostat.interface.timeseries import Timeseries
 from meteostat.interface.point import Point
 
 
-class Monthly(Base):
+class Monthly(Timeseries):
 
     """
     Retrieve monthly weather data for one or multiple weather stations or
@@ -30,21 +30,6 @@ class Monthly(Base):
     # The cache subdirectory
     cache_subdir: str = 'monthly'
 
-    # The list of weather Stations
-    _stations: pd.Index = None
-
-    # The start date
-    _start: datetime = None
-
-    # The end date
-    _end: datetime = None
-
-    # Include model data?
-    _model: bool = True
-
-    # The data frame
-    _data: pd.DataFrame = pd.DataFrame()
-
     # Default frequency
     _freq: str = '1MS'
 
@@ -144,14 +129,11 @@ def _load(
             # Get time index
             time = df.index.get_level_values('time')
 
-            # Filter & append
-            self._data = self._data.append(
-                df.loc[(time >= self._start) & (time <= self._end)])
+            # Filter & return
+            return df.loc[(time >= self._start) & (time <= self._end)]
 
-        else:
-
-            # Append
-            self._data = self._data.append(df)
+        # Return
+        return df
 
     def _get_data(self) -> None:
         """
@@ -169,12 +151,10 @@ def _get_data(self) -> None:
                 ))
 
             # Data Processing
-            processing_handler(datasets, self._load, self.max_threads)
+            return processing_handler(datasets, self._load, self.cores, self.threads)
 
-        else:
-
-            # Empty DataFrame
-            self._data = pd.DataFrame(columns=[*self._types])
+        # Empty DataFrame
+        return pd.DataFrame(columns=[*self._types])
 
     def _resolve_point(
         self,
@@ -265,7 +245,7 @@ def __init__(
         self._model = model
 
         # Get data for all weather stations
-        self._get_data()
+        self._data = self._get_data()
 
         # Interpolate data
         if isinstance(loc, Point):
@@ -282,14 +262,3 @@ def expected_rows(self) -> int:
 
         return ((self._end.year - self._start.year) * 12 +
                 self._end.month - self._start.month) + 1
-
-    # Import methods
-    from meteostat.series.normalize import normalize
-    from meteostat.series.interpolate import interpolate
-    from meteostat.series.aggregate import aggregate
-    from meteostat.series.convert import convert
-    from meteostat.series.coverage import coverage
-    from meteostat.series.count import count
-    from meteostat.series.fetch import fetch
-    from meteostat.series.stations import stations
-    from meteostat.core.cache import clear_cache
diff --git a/meteostat/interface/normals.py b/meteostat/interface/normals.py
index 4a345e1..a00cc89 100644
--- a/meteostat/interface/normals.py
+++ b/meteostat/interface/normals.py
@@ -117,17 +117,10 @@ def _load(
             # Get time index
             end = df.index.get_level_values('end')
 
-            # Filter & append
-            self._data = self._data.append(
-                df.loc[end == self._end])
+            # Filter & return
+            return df.loc[end == self._end]
 
-        else:
-
-            # Append
-            if self._data.index.size == 0:
-                self._data = df
-            else:
-                self._data = self._data.append(df)
+        return df
 
     def _get_data(self) -> None:
         """
@@ -145,12 +138,11 @@ def _get_data(self) -> None:
                 ))
 
             # Data Processing
-            processing_handler(datasets, self._load, self.max_threads)
-
-        else:
+            return processing_handler(
+                datasets, self._load, self.cores, self.threads)
 
-            # Empty DataFrame
-            self._data = pd.DataFrame(columns=[*self._types])
+        # Empty DataFrame
+        return pd.DataFrame(columns=[*self._types])
 
     def _resolve_point(
         self,
@@ -247,7 +239,7 @@ def __init__(
         self._end = end
 
         # Get data for all weather stations
-        self._get_data()
+        self._data = self._get_data()
 
         # Interpolate data
         if isinstance(loc, Point):
diff --git a/meteostat/interface/point.py b/meteostat/interface/point.py
index 6e50a6a..3004db4 100644
--- a/meteostat/interface/point.py
+++ b/meteostat/interface/point.py
@@ -40,6 +40,9 @@ class Point:
     # Altitude Weight
     weight_alt: float = 0.4
 
+    # The list of weather stations
+    stations: pd.Index = None
+
     # The latitude
     _lat: float = None
 
@@ -106,6 +109,9 @@ def get_stations(self, freq: str = None, start: datetime = None,
         # Sort by score (descending)
         stations = stations.sort_values('score', ascending=False)
 
+        # Capture result
+        self.stations = stations.index[:self.max_count]
+
         return stations.head(self.max_count)
 
     @property
diff --git a/meteostat/interface/stations.py b/meteostat/interface/stations.py
index e65d6c3..849da8b 100644
--- a/meteostat/interface/stations.py
+++ b/meteostat/interface/stations.py
@@ -8,10 +8,10 @@
 The code is licensed under the MIT license.
 """
 
-from math import cos, sqrt, radians
 from copy import copy
 from datetime import datetime, timedelta
 from typing import Union
+import numpy as np
 import pandas as pd
 from meteostat.core.cache import get_file_path, file_in_cache
 from meteostat.core.loader import load_handler
@@ -128,19 +128,27 @@ def nearby(
         temp = copy(self)
 
         # Calculate distance between weather station and geo point
-        def distance(station, point) -> float:
-            # Earth radius in m
+        def distance(lat1, lon1, lat2, lon2):
+            # Earth radius in meters
             radius = 6371000
 
-            x = (radians(point[1]) - radians(station['longitude'])) * \
-                cos(0.5 * (radians(point[0]) + radians(station['latitude'])))
-            y = (radians(point[0]) - radians(station['latitude']))
+            # Degress to radian
+            lat1, lon1, lat2, lon2 = map(np.deg2rad, [lat1, lon1, lat2, lon2])
 
-            return radius * sqrt(x * x + y * y)
+            # Deltas
+            dlat = lat2 - lat1
+            dlon = lon2 - lon1
 
-        # Get distance for each stationsd
-        temp._data['distance'] = temp._data.apply(
-            lambda station: distance(station, [lat, lon]), axis=1)
+            # Calculate distance
+            arch = np.sin(dlat / 2)**2 + np.cos(lat1) * \
+                np.cos(lat2) * np.sin(dlon / 2)**2
+            arch_sin = 2 * np.arcsin(np.sqrt(arch))
+
+            return radius * arch_sin
+
+        # Get distance for each station
+        temp._data['distance'] = distance(
+            lat, lon, temp._data['latitude'], temp._data['longitude'])
 
         # Filter by radius
         if radius is not None:
diff --git a/meteostat/interface/timeseries.py b/meteostat/interface/timeseries.py
new file mode 100644
index 0000000..35f36c8
--- /dev/null
+++ b/meteostat/interface/timeseries.py
@@ -0,0 +1,49 @@
+"""
+Timeseries Class
+
+Meteorological data provided by Meteostat (https://dev.meteostat.net)
+under the terms of the Creative Commons Attribution-NonCommercial
+4.0 International Public License.
+
+The code is licensed under the MIT license.
+"""
+
+from datetime import datetime
+import pandas as pd
+from meteostat.interface.base import Base
+
+
+class Timeseries(Base):
+
+    """
+    Timeseries class which provides features which are used across all time series classes
+    """
+
+    # The list of weather Stations
+    _stations: pd.Index = None
+
+    # The list of origin weather Stations
+    _origin_stations: pd.Index = None
+
+    # The start date
+    _start: datetime = None
+
+    # The end date
+    _end: datetime = None
+
+    # Include model data?
+    _model: bool = True
+
+    # The data frame
+    _data: pd.DataFrame = pd.DataFrame()
+
+    # Import methods
+    from meteostat.series.normalize import normalize
+    from meteostat.series.interpolate import interpolate
+    from meteostat.series.aggregate import aggregate
+    from meteostat.series.convert import convert
+    from meteostat.series.coverage import coverage
+    from meteostat.series.count import count
+    from meteostat.series.fetch import fetch
+    from meteostat.series.stations import stations
+    from meteostat.core.cache import clear_cache
diff --git a/meteostat/series/normalize.py b/meteostat/series/normalize.py
index 0a2eec1..506c477 100644
--- a/meteostat/series/normalize.py
+++ b/meteostat/series/normalize.py
@@ -26,46 +26,48 @@ def normalize(self):
     # Create temporal instance
     temp = copy(self)
 
-    # Create result DataFrame
-    result = pd.DataFrame(columns=temp._columns[temp._first_met_col:])
-
-    # Handle tz-aware date ranges
-    if hasattr(temp, '_timezone') and temp._timezone is not None:
-        timezone = pytz.timezone(temp._timezone)
-        start = temp._start.astimezone(timezone)
-        end = temp._end.astimezone(timezone)
-    else:
-        start = temp._start
-        end = temp._end
-
-    # Go through list of weather stations
-    for station in temp._stations:
-        # Create data frame
-        df = pd.DataFrame(columns=temp._columns[temp._first_met_col:])
-        # Add time series
-        df['time'] = pd.date_range(
-            start,
-            end,
-            freq=self._freq,
-            tz=temp._timezone if hasattr(temp, '_timezone') else None)
-        # Add station ID
-        df['station'] = station
-        # Add columns
-        for column in temp._columns[temp._first_met_col:]:
-            # Add column to DataFrame
-            df[column] = NaN
-
-        result = pd.concat([result, df], axis=0)
-
-    # Set index
-    result = result.set_index(['station', 'time'])
-
-    # Merge data
-    temp._data = pd.concat([temp._data, result], axis=0).groupby(
-        ['station', 'time'], as_index=True).first()
-
-    # None -> NaN
-    temp._data = temp._data.fillna(NaN)
+    if temp._start and temp._end and temp.coverage() < 1:
+
+        # Create result DataFrame
+        result = pd.DataFrame(columns=temp._columns[temp._first_met_col:])
+
+        # Handle tz-aware date ranges
+        if hasattr(temp, '_timezone') and temp._timezone is not None:
+            timezone = pytz.timezone(temp._timezone)
+            start = temp._start.astimezone(timezone)
+            end = temp._end.astimezone(timezone)
+        else:
+            start = temp._start
+            end = temp._end
+
+        # Go through list of weather stations
+        for station in temp._stations:
+            # Create data frame
+            df = pd.DataFrame(columns=temp._columns[temp._first_met_col:])
+            # Add time series
+            df['time'] = pd.date_range(
+                start,
+                end,
+                freq=self._freq,
+                tz=temp._timezone if hasattr(temp, '_timezone') else None)
+            # Add station ID
+            df['station'] = station
+            # Add columns
+            for column in temp._columns[temp._first_met_col:]:
+                # Add column to DataFrame
+                df[column] = NaN
+
+            result = pd.concat([result, df], axis=0)
+
+        # Set index
+        result = result.set_index(['station', 'time'])
+
+        # Merge data
+        temp._data = pd.concat([temp._data, result], axis=0).groupby(
+            ['station', 'time'], as_index=True).first()
+
+        # None -> NaN
+        temp._data = temp._data.fillna(NaN)
 
     # Return class instance
     return temp
diff --git a/setup.py b/setup.py
index 63e0b49..cfa9d9b 100644
--- a/setup.py
+++ b/setup.py
@@ -15,7 +15,7 @@
 # Setup
 setup(
     name='meteostat',
-    version='1.4.6',
+    version='1.5.0',
     author='Meteostat',
     author_email='info@meteostat.net',
     description='Access and analyze historical weather and climate data with Python.',