Commit ea881256 authored by tills's avatar tills
Browse files

Hallo Sophia

parent 90d06782
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
import pandas as pd import pandas as pd
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import os import os
import scipy import scipy
import seaborn as sns import seaborn as sns
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
file=os.getcwd() file=os.getcwd()
file_m=file[0:(len(file)-9)] file_m=file[0:(len(file)-9)]
file_dz='data\\modeling\\train' file_dz='data\\modeling\\train'
file_data=file_m+file_dz file_data=file_m+file_dz
data_name=os.listdir(file_data) data_name=os.listdir(file_data)
data='train_data' data='train_data'
file_number=file_m+file_dz+'\\'+data+'.txt' file_number=file_m+file_dz+'\\'+data+'.txt'
df = pd.read_csv(file_number) df = pd.read_csv(file_number)
df["collection_intervall"] = list(map(lambda st: str(st)[0:int(st.index("d"))],df["last_collection"])) df["collection_intervall"] = list(map(lambda st: str(st)[0:int(st.index("d"))],df["last_collection"]))
df["collection_intervall"]=df["collection_intervall"].astype(int) df["collection_intervall"]=df["collection_intervall"].astype(int)
df["collection_intervall"]= list(map(lambda z: z*(-1),df["collection_intervall"])) df["collection_intervall"]= list(map(lambda z: z*(-1),df["collection_intervall"]))
df['lockdown']= list(map(lambda z: 'Ja' if z==1 else 'Nein',df['Lockdown'])) df['lockdown']= list(map(lambda z: 'Ja' if z==1 else 'Nein',df['Lockdown']))
df df
``` ```
%% Output %% Output
Unnamed: 0 Unnamed: 0.1 timestamp container_id \ Unnamed: 0 Unnamed: 0.1 timestamp container_id \
0 0 0 2020-05-22 18:51:01.742945 70B3D500700016DA 0 0 0 2020-05-22 18:51:01.742945 70B3D500700016DA
1 1 1 2020-06-05 14:49:42.681218 70B3D500700016DA 1 1 1 2020-06-05 14:49:42.681218 70B3D500700016DA
2 2 2 2020-06-29 13:47:52.050553 70B3D500700016DA 2 2 2 2020-06-29 13:47:52.050553 70B3D500700016DA
3 3 3 2020-07-17 13:46:18.287249 70B3D500700016DA 3 3 3 2020-07-17 13:46:18.287249 70B3D500700016DA
4 4 4 2020-08-07 09:44:36.149679 70B3D500700016DA 4 4 4 2020-08-07 09:44:36.149679 70B3D500700016DA
.. ... ... ... ... .. ... ... ... ...
627 4381 4381 2020-08-08 15:42:32.866709 70B3D50070001786 627 4381 4381 2020-08-08 15:42:32.866709 70B3D50070001786
628 4382 4382 2020-08-09 15:42:30.118122 70B3D50070001786 628 4382 4382 2020-08-09 15:42:30.118122 70B3D50070001786
629 4383 4383 2020-08-11 12:42:24.962069 70B3D50070001786 629 4383 4383 2020-08-11 12:42:24.962069 70B3D50070001786
630 4384 4384 2020-09-07 13:40:11.695782 70B3D50070001786 630 4384 4384 2020-09-07 13:40:11.695782 70B3D50070001786
631 4385 4385 2020-09-14 15:39:33.709211 70B3D50070001786 631 4385 4385 2020-09-14 15:39:33.709211 70B3D50070001786
last_collection pre_height post_height \ last_collection pre_height post_height \
0 -14 days +06:00:58.208000 136 16 0 -14 days +06:00:58.208000 136 16
1 -14 days +04:01:19.058000 120 14 1 -14 days +04:01:19.058000 120 14
2 -24 days +01:01:50.633000 136 14 2 -24 days +01:01:50.633000 136 14
3 -18 days +00:01:33.806000 128 12 3 -18 days +00:01:33.806000 128 12
4 -21 days +04:01:42.126000 118 14 4 -21 days +04:01:42.126000 118 14
.. ... ... ... .. ... ... ...
627 -2 days +21:00:01.687000 64 28 627 -2 days +21:00:01.687000 64 28
628 -1 days +00:00:02.748000 60 24 628 -1 days +00:00:02.748000 60 24
629 -2 days +03:00:05.138000 70 30 629 -2 days +03:00:05.138000 70 30
630 -28 days +23:02:13.362000 62 30 630 -28 days +23:02:13.362000 62 30
631 -8 days +22:00:37.993000 64 28 631 -8 days +22:00:37.993000 64 28
sensor_mean_temperature sensor_max_temperature sensor_min_temperature \ sensor_mean_temperature sensor_max_temperature sensor_min_temperature \
0 15.251029 47 0 0 15.251029 47 0
1 16.410714 44 4 1 16.410714 44 4
2 18.255446 43 4 2 18.255446 43 4
3 19.053476 45 7 3 19.053476 45 7
4 21.981524 47 6 4 21.981524 47 6
.. ... ... ... .. ... ... ...
627 33.296296 59 16 627 33.296296 59 16
628 32.217391 60 15 628 32.217391 60 15
629 28.121951 60 15 629 28.121951 60 15
630 20.341463 55 7 630 20.341463 55 7
631 20.869281 44 6 631 20.869281 44 6
... weather_max_moisture weather_min_moisture holiday_percentage \ ... weather_max_moisture weather_min_moisture holiday_percentage \
0 ... 95.0 25.0 0.360606 0 ... 95.0 25.0 0.360606
1 ... 93.0 19.0 0.361446 1 ... 93.0 19.0 0.361446
2 ... 97.0 25.0 0.375652 2 ... 97.0 25.0 0.375652
3 ... 96.0 22.0 0.222222 3 ... 96.0 22.0 0.222222
4 ... 95.0 20.0 0.288000 4 ... 95.0 20.0 0.288000
.. ... ... ... ... .. ... ... ... ...
627 ... 64.0 23.0 0.592593 627 ... 64.0 23.0 0.592593
628 ... 75.0 21.0 1.000000 628 ... 75.0 21.0 1.000000
629 ... 69.0 23.0 0.177778 629 ... 69.0 23.0 0.177778
630 ... 98.0 28.0 0.295840 630 ... 98.0 28.0 0.295840
631 ... 94.0 26.0 0.282353 631 ... 94.0 26.0 0.282353
Lockdown year month weekday collection_intervall number_collections \ Lockdown year month weekday collection_intervall number_collections \
0 0.0 2020 5 4 14 1 0 0.0 2020 5 4 14 1
1 0.0 2020 6 4 14 1 1 0.0 2020 6 4 14 1
2 0.0 2020 6 0 24 1 2 0.0 2020 6 0 24 1
3 0.0 2020 7 4 18 1 3 0.0 2020 7 4 18 1
4 0.0 2020 8 4 21 1 4 0.0 2020 8 4 21 1
.. ... ... ... ... ... ... .. ... ... ... ... ... ...
627 0.0 2020 8 5 2 1 627 0.0 2020 8 5 2 1
628 0.0 2020 8 6 1 1 628 0.0 2020 8 6 1 1
629 0.0 2020 8 1 2 1 629 0.0 2020 8 1 2 1
630 0.0 2020 9 0 28 1 630 0.0 2020 9 0 28 1
631 0.0 2020 9 0 8 1 631 0.0 2020 9 0 8 1
lockdown lockdown
0 Nein 0 Nein
1 Nein 1 Nein
2 Nein 2 Nein
3 Nein 3 Nein
4 Nein 4 Nein
.. ... .. ...
627 Nein 627 Nein
628 Nein 628 Nein
629 Nein 629 Nein
630 Nein 630 Nein
631 Nein 631 Nein
[632 rows x 27 columns] [632 rows x 27 columns]
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
X_train=pd.DataFrame({'pre_height':df['pre_height'],'collection_intervall':df['collection_intervall'], X_train=pd.DataFrame({'pre_height':df['pre_height'],'collection_intervall':df['collection_intervall'],
'Lockdown':df['lockdown']}) 'Lockdown':df['lockdown']})
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
g=sns.jointplot(data=X_train,x='collection_intervall',y='pre_height',hue='Lockdown',height=10) g=sns.jointplot(data=X_train,x='collection_intervall',y='pre_height',hue='Lockdown',height=10)
g.ax_joint.set_xlabel('Sammulungs Intervall',fontweight='bold') g.ax_joint.set_xlabel('Sammulungs Intervall',fontweight='bold')
g.ax_joint.set_ylabel('Höhe vor Leerung',fontweight='bold') g.ax_joint.set_ylabel('Höhe vor Leerung',fontweight='bold')
g.ax_joint.set_title('Auswirkungen Lockdown auf Höhe vor Leerung und Sammlungs Intervall',fontweight='bold') g.ax_joint.set_title('Auswirkungen Lockdown auf Höhe vor Leerung und Sammlungs Intervall',fontweight='bold')
``` ```
%% Output %% Output
Text(0.5, 1.0, 'Auswirkungen Lockdown auf Höhe vor Leerung und Sammlungs Intervall') Text(0.5, 1.0, 'Auswirkungen Lockdown auf Höhe vor Leerung und Sammlungs Intervall')
%% Cell type:code id: tags:
``` python
# hallo Sophia
```
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment