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Commit 849e0a9c authored by Kalter's avatar Kalter
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Initial commit of market analysis script

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%% Cell type:code id: tags:
``` python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
```
%% Cell type:code id: tags:
``` python
def cleanup_manufacturers(df:pd.DataFrame, manufacturer_column = "Hersteller"):
if manufacturer_column not in df:
return
def plot_histogram(df:pd.DataFrame, column_name:str, bins:float|list[float]=20, xlim:list[float]=[None,None]):
if column_name in df.columns:
fig, ax = plt.subplots()
# Plot the histogram
options = {
"bins":bins, "color":'red', "edgecolor":'black',
"histtype":"bar", "rwidth":0.8, "log":False
}
ax.hist(df[column_name], **options)
ax.set_title('Histogram of ' + column_name)
ax.set_xlabel(column_name)
ax.set_ylabel('Frequency')
ax.set_xlim(xlim)
ax.tick_params(axis="x", rotation=45)
plt.show()
else:
print(f"Column '{column_name}' not found in the data.")
return
def plot_barchart(df:pd.DataFrame, column_name:str, num_entries:float|list[float]=None, sort:bool = True, lower_threshold=None):
if column_name in df.columns:
fig, ax = plt.subplots()
value_counts = df[column_name].value_counts()
if sort:
print("Sorting ...")
value_counts = value_counts.sort_values()[::-1]
if sort and num_entries:
print(f"Sorting and choosing first {num_entries} entries ...")
value_counts = value_counts[:num_entries]
if sort and lower_threshold and not num_entries:
print(f"Sorting and cutting all values smaller than {lower_threshold} ...")
value_counts = df[column_name].value_counts().sort_values()[::-1][:num_entries]
value_counts = value_counts[value_counts.values>lower_threshold]
print(value_counts)
categories = value_counts.index
frequencies = value_counts.values
options = {
"color":"red", "edgecolor":"black"
}
ax.bar(categories, frequencies, **options)
ax.set_title('Bar Chart of ' + column_name)
ax.set_xlabel(column_name)
ax.set_ylabel('Frequency')
# Rotate x-tick labels
ax.tick_params(axis='x', rotation=90)
plt.show()
else:
print(f"Column '{column_name}' not found in the data.")
return
```
%% Cell type:code id: tags:
``` python
file_path = "N:\Forschung\EBC1100_ERS_RollOut_KAP_FCN\Data\Literatur\BAFA_Förderfähige_WP_nka.xlsx"
types = {'Hersteller':str, 'Typ ':str, 'Wärme-Nennleistung 35°C [kW]':np.float64, 'ETAs 35 [%]':np.float64,
'Wärme-Nennleistung 55°C [kW]':np.float64, 'ETAs 55 [%]':np.float64, 'Kältemittel':str,
'Netzdienlichkeit':str, 'EE-Anzeige':str}
lw = pd.read_excel(file_path, sheet_name="Luft-Wasser", decimal=",", thousands=".",
dtype=types)
print(lw.columns)
```
%% Output
Index(['Hersteller', 'Typ ', 'Wärme-Nennleistung 35°C [kW]', 'ETAs 35 [%]',
'Wärme-Nennleistung 55°C [kW]', 'ETAs 55 [%]', 'Kältemittel',
'Netzdienlichkeit', 'EE-Anzeige'],
dtype='object')
%% Cell type:code id: tags:
``` python
bins = [*np.arange(0,1000,10), 2000]
print(bins)
#plot_histogram(lw, 'Wärme-Nennleistung 35°C [kW]', bins=bins, xlim=[0,1000])
plot_barchart(lw, 'Hersteller', num_entries=None, lower_threshold=50)
```
%% Output
[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810, 820, 830, 840, 850, 860, 870, 880, 890, 900, 910, 920, 930, 940, 950, 960, 970, 980, 990, 2000]
Sorting ...
Sorting and cutting all values smaller than 50 ...
Hersteller
Mitsubishi Electric Europe B.V 1316
DAIKIN Airconditioning Germany GmbH 221
Trane 204
Buderus - Bosch Thermotechnik GmbH 184
Clivet 174
Panasonic Deutschland 129
AERMEC GmbH 128
FläktGroup 124
ELCO GmbH 97
Toshiba - Beijer Ref Deutschland GmbH 92
Viessmann Werke GmbH & Co KG 88
Bosch Thermotechnik GmbH 82
Samsung Klimatechnik 67
TCL 62
alpha innotec (ait-deutschland GmbH) 62
Swegon Operation srl 60
HYUNDAI 59
NOVELAN (ait-deutschland GmbH) 59
Kampmann 58
S-Klima 56
Inventor 54
Malag&Soltau GmbH / M&S POWER 54
Hitachi - Johnson Controls Hitachi Air\nConditioning Europe SAS 53
Ningbo AUX Electric Co., Ltd. 53
Dimplex - Glen Dimplex Deutschland GmbH 51
Name: count, dtype: int64
%% Cell type:code id: tags:
``` python
```
%% Cell type:code id: tags:
``` python
```
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