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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyse der Temperaturentwicklung in den Jahren 1961 - 2010"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In diesem Notebook soll untersucht werden, wie sich die Anzahl der heißen und Sommertage im Zeitraum von 1961 - 2010 verändert haben. Dabei können folgende Fragestellungen untersucht werden:\n",
"\n",
"- Allgemeine Veränderung in Deutschland. Gibt es Orte, an denen es weniger heiße Tage gibt?\n",
"- Wie verhält sich die Niederschlagsmenge an denselben Orten, nimmt diese zu?\n",
"- Existiert eine Korrelation zwischen den beiden Varianten\n",
"\n",
"Als Daten werden die vieljährigen Mittel vom [CDC_Center](ftp://ftp-cdc.dwd.de/pub/CDC/observations_germany/climate/multi_annual/) genommen. Um die geringste Überschneidung zu haben werden die Daten von 1961-1990 und 1981-2010 genommen."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dateneinlese\n",
"\n",
"Um die oben genannten Effekte zu untersuchen, wird die Veränderung der Anzahl der Sommertage (Tage, die wärmer als 25°C waren) und die Menge an Jahresniederschlag in den beiden Datenreihen zwischen 1961-1990 und 1981-2010 verglichen."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Datenbearbeitung / Data wrangling"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"# Notwendige Bibliotheken\n",
"# Read in libraries\n",
"%matplotlib inline \n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as matplt\n",
"import scipy as sp\n",
"\n",
"# Einlesen der Wetterdaten (Sommertage und Niederschlag)\n",
"summer_days_61_90 = pd.read_csv(\"Sommertage_1961-1990_festerStandort.txt\", encoding = \"latin-1\", delimiter = \";\")\n",
"summer_days_81_10 = pd.read_csv(\"Sommertage_1981-2010_festerStandort.txt\", encoding = \"latin-1\", delimiter = \";\")\n",
"precipitation_61_90 =pd.read_csv(\"Niederschlag_1961-1990_festerStandort.txt\", encoding = \"latin-1\", delimiter = \";\")\n",
"precipitation_81_10 = pd.read_csv(\"Niederschlag_1981-2010_festerStandort.txt\", encoding = \"latin-1\", delimiter = \";\")\n",
"\n",
"# Stationsnamen und -id einlesen\n",
"stationsnamen_1961_1990 = pd.read_csv(\"Sommertage_1961-1990_Stationsliste_festerStandort.txt\", encoding = \"latin-1\", delimiter = \";\")\n",
"stationsnamen_1981_2010 = pd.read_csv(\"Sommertage_1981-2010_Stationsliste_festerStandort.txt\", encoding = \"latin-1\", delimiter = \";\")\n",
"stationsnamen_precipitation_61_90 = pd.read_csv(\"Niederschlag_1961-1990_Stationsliste_festerStandort.txt\", encoding = \"latin-1\", delimiter = \";\")\n",
"stationsnamen_precipitation_81_90 = pd.read_csv(\"Niederschlag_1981-2010_Stationsliste_festerStandort.txt\", encoding = \"latin-1\", delimiter = \";\") "
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"# Funktionen erstellen, um Daten zu bearbeiten\n",
"# Create functions for data wrangling\n",
"\n",
"def drop_last_n_rows(dataframe,number_of_rows):\n",
" \"\"\" Nimmt einen DataFrame als Input und löscht die letzten number_of_rows des DataFrames und gibt es zurück //\n",
" Takes in a dataframe and chops of the number_of_rows at the end and returns it\"\"\"\n",
"\n",
" return dataframe[:-number_of_rows]\n",
" \n",
"\n",
"def create_analyses_dataframe(list_names,dataframe1,dataframe2):\n",
" \"\"\" Nimmt zwei DataFrames als Input und eine Liste mit Spaltennamen (list_names). Gibt einen DataFrame mit den in list_names\n",
" angegebenen Spalten zurück\n",
" \n",
" Takes in two dataframes and returns one data frame with the desired column names specified in list_names. List_names\n",
" must be a string. It also checks whether columns were entered multiple times (due to merging). Deletes first occurence \n",
" of said column\"\"\"\n",
" \n",
" # DataFrame zusammenfügen concatenate\n",
" # Concatenate both initial DataFrames\n",
" \n",
" dataframeTot = pd.concat([dataframe1,dataframe2], axis = 1, join_axes = [dataframe1.index]) # axis = 1 => horizontal concatenation\n",
" \n",
" # Leeren DataFrame Container erstellen als return-DataFrame\n",
" # Create empty DataFrame as return-DataFrame\n",
" \n",
" dataframeContainer = pd.DataFrame()\n",
" \n",
" i = 0 \n",
" for list_entry in list_names:\n",
" if i == 0:# Initialbedingung / initial condition\n",
" i = i+1\n",
" dataframeContainer = pd.concat([dataframeContainer, dataframeTot[list_entry]], axis = 1)\n",
" else:\n",
" dataframeContainer = pd.concat([dataframeContainer,dataframeTot[list_entry]], axis = 1)\n",
" \n",
" # Überprüfe für doppelte Spalteneinträge\n",
" # Check for double entries\n",
" \n",
" for name in dataframeContainer.columns:\n",
" # Geht durch die Liste der Spaltennamen und prüft, ob es doppelte Einträge gibt. Wenn ja, reduziert es auf einen \n",
" # cycles through list of column names and checks whether there are duplicates. If there are, removes duplicate and adds a single version of the column to the end\n",
" if name in dataframeContainer.columns:\n",
" \n",
" seriesCopy = dataframe1[name] # Kopie erstellen, dann doppelte Einträge löschen. Get copy, then delete duplicate columns and add series again\n",
" del dataframeContainer[name]\n",
" \n",
" dataframeContainer = pd.concat([dataframeContainer, seriesCopy], axis = 1)\n",
" return dataframeContainer\n",
"\n",
" return dataframeContainer\n",
"\n",
"def strip(string_value):\n",
" \"\"\" Nimmt einen string als Input und löscht alle Leerzeichen am Anfang und Ende. Genutzt für .apply()\n",
" \n",
" Takes a string as input and strips leading and trailing whitespace. Used for apply functions\"\"\"\n",
" \n",
" return string_value.strip()\n",
"\n",
"def strip_and_parse(DataFrame):\n",
" \"\"\" Funktion nimmt einen DataFrame als Input und löscht alle Leerzeichen am Anfang und Ende. Gibt den DataFrame bearbeitet zurück.\n",
" \n",
" This function takes in a dataframe and strips all leading and trailing whitespaces and returns the\n",
" entire dataframe again. Checks if column is really of type string (object in python)\"\"\"\n",
" \n",
" # Typen der Spalten bekommen für alle Spalteneinträge des DataFrames\n",
" # get type of column for entire dataframe\n",
" dtype_list = DataFrame.dtypes\n",
" \n",
" for entry, column_name in zip(dtype_list, DataFrame.columns):\n",
" \n",
" # zip-Funktion verbindet einzelne Elemente in einen Tuple. So wird jedem Spaltennamen der korrespondierende Typ zugeordnet.\n",
" # zip function combines two seperate elements into one tuple. So every column type has its corresponding column_name with it\n",
" if entry == object:\n",
" \n",
" DataFrame[column_name] = DataFrame[column_name].apply(strip)\n",
" \n",
" return DataFrame\n",
"\n",
"def parse_data_to_int(DataFrame, column_name):\n",
" \"\"\" Nimmt einen DataFrame und Spaltennamen (column_name) und konvertiert Einträge zum Typ integer\n",
" \n",
" Takes in a DataFrame and a column name and converts values to from type object(string) to integer\"\"\"\n",
" \n",
" DataFrame[column_name] = DataFrame[column_name].apply(int)\n",
" \n",
" return DataFrame\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"# Letzte Zeilen zeigen fehlerhafte Einträge auf --> löschen\n",
"# cut off last row entry\n",
"\n",
"summer_days_61_90_dropped = drop_last_n_rows(summer_days_61_90,1)\n",
"summer_days_81_10_dropped = drop_last_n_rows(summer_days_81_10,1)\n",
"stationsnamen_1961_1990_dropped = drop_last_n_rows(stationsnamen_1961_1990,1)\n",
"stationsnamen_1981_2010_dropped = drop_last_n_rows(stationsnamen_1981_2010,1)\n",
"precipitation_61_90_dropped = drop_last_n_rows(precipitation_61_90,2) # for some reason station name is missing one id so we need to drop the trailing 2 values\n",
"precipitation_81_10_dropped = drop_last_n_rows(precipitation_81_10,2)\n",
"stationsnamen_precipitation_61_90_dropped = drop_last_n_rows(stationsnamen_precipitation_61_90,1)\n",
"stationsnamen_precipitation_81_10_dropped = drop_last_n_rows(stationsnamen_precipitation_81_90,1)\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"# Liste der Spaltennamen erstellen, um den gewünschten DataFrame zu bekommen.\n",
"# Creation of a list with Stations_ids, Name, geogr. Breite und Jahresdurchschnittswert (which translates to Stations_id, name, lattitude and average of value)\n",
"\n",
"column_names = [\"Stations_id\",\"Stationsname\",\"geogr. Breite\", \"Jahr\"]\n",
"\n",
"# Verbinden der Stationsnamen und -id mit den Messdaten\n",
"# concatenate ids and names of weather stations with its data and latitude\n",
"\n",
"summer_days_61_90_concat = create_analyses_dataframe(column_names, summer_days_61_90_dropped, stationsnamen_1961_1990_dropped)\n",
"summer_days_81_10_concat = create_analyses_dataframe(column_names, summer_days_81_10_dropped,stationsnamen_1981_2010_dropped)\n",
"precipitation_61_90_concat = create_analyses_dataframe(column_names, precipitation_61_90_dropped,stationsnamen_precipitation_61_90_dropped)\n",
"precipitation_81_10_concat = create_analyses_dataframe(column_names, precipitation_81_10_dropped, stationsnamen_precipitation_81_10_dropped)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"# Spalte \"Stations_id\" ist Typ object (string). Leerzeichen löschen\n",
"# Convert string of Stations_id to int value\n",
"\n",
"summer_days_61_90_concat = strip_and_parse(summer_days_61_90_concat)\n",
"summer_days_81_10_concat = strip_and_parse(summer_days_81_10_concat)\n",
"precipitation_61_90_concat = strip_and_parse(precipitation_61_90_concat)\n",
"precipitation_81_10_concat = strip_and_parse(precipitation_81_10_concat)\n",
"\n",
"# Stations_id zu Typ int machen.\n",
"# parse the Station_id column to int so it can be used as an index\n",
"\n",
"summer_days_61_90_concat = parse_data_to_int(summer_days_61_90_concat, \"Stations_id\")\n",
"summer_days_81_10_concat = parse_data_to_int(summer_days_81_10_concat, \"Stations_id\")\n",
"precipitation_61_90_concat = parse_data_to_int(precipitation_61_90_concat, \"Stations_id\")\n",
"precipitation_81_10_concat = parse_data_to_int(precipitation_81_10_concat, \"Stations_id\")"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"# Stations_id als neuen Index setzen\n",
"# Set index to stations_id\n",
"\n",
"summer_days_61_90_concat = summer_days_61_90_concat.set_index(\"Stations_id\")\n",
"summer_days_81_10_concat = summer_days_81_10_concat.set_index(\"Stations_id\")\n",
"precipitation_61_90_concat = precipitation_61_90_concat.set_index(\"Stations_id\")\n",
"precipitation_81_10_concat = precipitation_81_10_concat.set_index(\"Stations_id\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Datenanalyse / Data analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sommertage / Summer days"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"# Berechnung wie sich Anzahl an Sommertage pro Jahr geändert haben\n",
"# Calculate change of summer days per year for the two periods\n",
"\n",
"differenz_sommertage = summer_days_81_10_concat[\"Jahr\"] - summer_days_61_90_concat[\"Jahr\"]\n",
"\n",
"# NAN entfernen / delete NAN\n",
"differenz_sommertage_nan_dropped = differenz_sommertage.dropna()\n",
"\n",
"\n",
"# Daten bearbeiten, um geogr. Breite zu analysieren\n",
"# Continued wrangling for latitude analysis\n",
"\n",
"differenz_sommertage_nan_dropped.index\n",
"\n",
"# Addieren dividieren durch 2 der geogr. Breiten, um korrekten Indexmatch der beiden Dataframe zu bekommen.\n",
"# add and divide by two of geogr. breite column to get correct indeces of both columns\n",
"sum_of_latitude = (summer_days_61_90_concat[\"geogr. Breite\"] + summer_days_81_10_concat[\"geogr. Breite\"])/2\n",
"\n",
"# NAN entfernen / Delete NAN\n",
"latitude_pos = sum_of_latitude.dropna()\n",
"\n",
"# Regressiondaten berechnen\n",
"# calcualte regression data \n",
"slope, intercept, r_value, p_value, std_err = sp.stats.linregress(latitude_pos,differenz_sommertage_nan_dropped)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Niederschlag / Precipitation"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"# Differenz in der Niederschlagsmenge für die Perioden berechnen.\n",
"# Calculate difference of precipitation between the years\n",
"\n",
"differenz_precipitation = precipitation_81_10_concat[\"Jahr\"] - precipitation_61_90_concat[\"Jahr\"]\n",
"\n",
"# NAN entfernen / Drop NAN\n",
"differenz_precipitation_nan_dropped = differenz_precipitation.dropna()\n",
"\n",
"# Addieren dividieren durch 2 der geogr. Breiten, um korrekten Indexmatch der beiden Dataframe zu bekommen.\n",
"# add and divide by two of geogr. breite column to get correct indeces of both columns\n",
"\n",
"latitude_position_precipitation = (precipitation_81_10_concat[\"geogr. Breite\"] + precipitation_61_90_concat[\"geogr. Breite\"])/2\n",
"latitude_position_precipitation_dropped = latitude_position_precipitation.dropna()\n",
"\n",
"# Regressiondaten berechnen\n",
"# calcualte regression data \n",
"\n",
"slope, intercept, r_value, p_value, std_err = sp.stats.linregress(latitude_position_precipitation_dropped,differenz_precipitation_nan_dropped)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Diagramme für Sommertage/ Plots for summer days"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(16,0,'count 187.000000\\nmean 7.177005\\nstd 2.814699\\nmin -0.100000\\n25% 5.250000\\n50% 7.200000\\n75% 9.100000\\nmax 15.100000\\nName: Jahr, dtype: float64')"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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MOgFJEyQdIukJSbMljZE0UNKtkmZKulPS0oX1N5b0QB749XFJmxfS9pb0dM73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\nzcy6oc560093vEGhVpL2AvaJiHYbK03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+22Rfg4DlTbabFhEnNrOfj/PXleSf8zx+agpw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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x247643869e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plotte Veränderung der Sommertageanzahl als Histogram\n",
"# Plot change of summer days as histogram\n",
"\n",
"fig = matplt.hist(differenz_sommertage_nan_dropped, bins = 20)\n",
"matplt.title(\"Zunahme der Sommertage in Deutschland in den Zeiträumen 1961-1990 und 1981-2010\")\n",
"matplt.xlabel(\"Differenz der Sommertage\")\n",
"matplt.ylabel(\"Häufigkeit\")\n",
"matplt.text(16,0, str(differenz_sommertage_nan_dropped.describe()), fontsize = 12)\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(51,14,'r = 0.0707411043282')"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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HhsNhfeKJJ7SsrEz/9Kc/qarqli1bdO7cufqjH/0o5bn8whe+oJ/5zGd0z549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\nedK1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2476427b828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Regressionsplot für geogr. Breite und Veränderung der Sommertage\n",
"# Regression plot for latitude and change in summer days\n",
"\n",
"g = sns.regplot(latitude_pos, differenz_sommertage_nan_dropped)\n",
"matplt.title(\"Änderung d. Anzahl an Sommertagen in Abhängigkeit der geogr. Breite zwichen 1961 - 2010\")\n",
"matplt.xlabel(\"Geographische Breite (Latitude)\")\n",
"matplt.ylabel(\"Veränderung d. Anzahl d. Sommertage\")\n",
"matplt.text(51,14, \"r = \" + str((r_value)), fontsize = 12)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Diagramme für Niederschlag / Plots for precipitation"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Text(260,0,'count 2260.000000\\nmean 29.614469\\nstd 34.074449\\nmin -272.200000\\n25% 9.800000\\n50% 29.850000\\n75% 47.900000\\nmax 234.400000\\nName: Jahr, dtype: float64')"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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zImJoRAyde+65O228mZmZ9a626ZMD/FfSnGTQIWk1ypmWjqwObC5pY2A6Sp+c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\nvl0cJE9j/6Ckj0TdsNBRhqZOs9OFEaqfXJh6KfN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"text/plain": [
"<matplotlib.figure.Figure at 0x24764de89e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plotte Veränderung der Niederschlangsmenge als Histogram\n",
"# Plot change of precipitation as histogram\n",
"\n",
"fig = matplt.hist(differenz_precipitation_nan_dropped, bins = 20)\n",
"axes = matplt.gca()\n",
"matplt.xlabel(\"Niederschlagsmengendifferenz [mm]\")\n",
"matplt.ylabel(\"Häufigkeit der Niederschlagsmengen\")\n",
"matplt.title(\"Differenz der Niederschlagsmengen in den Zeiträumen 1961-1990 und 1981-2010\")\n",
"axes.set_xlim([-200,250])\n",
"matplt.text(260,0, differenz_precipitation_nan_dropped.describe())"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'Differenz der Niederschlagsmenge in den Zeiträumen 1961-1990 und 1981 - 2010')"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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H/2YReQ54c/AdTFXCLcDzwPeBj06AzFWxYXMHV9z+FB29Gdoa43T0Zrji9qfs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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x24764204240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Regressionsplot für geogr. Breite und Veränderung der Niederschlagsmenge\n",
"# Regression plot for latitude and change in precipitation\n",
"\n",
"g = sns.regplot(latitude_position_precipitation_dropped,differenz_precipitation_nan_dropped)\n",
"matplt.ylabel(\"Niederschlagsmengenen Differenz in mm\")\n",
"matplt.text(52,210, \"r: \" + str(r_value))\n",
"matplt.title(\"Differenz der Niederschlagsmenge in den Zeiträumen 1961-1990 und 1981 - 2010\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:Anaconda]",
"language": "python",
"name": "conda-env-Anaconda-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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