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Copy pathDKB-Kontoauszug-Visualizer.py
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DKB-Kontoauszug-Visualizer.py
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# coding: utf-8
# In[27]:
import pandas as pd
get_ipython().magic(u'matplotlib inline')
# 
# In[28]:
knr = '1808150815' # type in your KNr here!
kst = 1000.0 # EUR zu Beginn
# In[29]:
data = pd.read_csv(knr+'.csv', index_col='Buchungstag',
header=4, sep=';', quoting=1, parse_dates=True,
encoding='latin-1', dayfirst=True)
# In[30]:
#data.head(5)
# In[ ]:
# ### Fix European Thousands and Comma String Problem
# In[31]:
data['Betrag (EUR)'] = data['Betrag (EUR)'].str.replace('.','')
data['Betrag (EUR)'] = data['Betrag (EUR)'].str.replace(',','.')
data['Betrag (EUR)'] = data['Betrag (EUR)'].astype('float')
# In[32]:
# Anon the data for public
#import random
#data['Betrag (EUR)'] = [-50+random.random()*200 for rv in range(len(data['Betrag (EUR)']))]
#data[u'Auftraggeber / Begünstigter'] = ['Empf/Sender '+str(i) for i in range(len(data['Betrag (EUR)']))]
# ### Rename some things
# In[33]:
data[u'Auftraggeber / Begünstigter'].replace('DKB AG', 'DKB Broker', inplace=True)
data[u'Auftraggeber / Begünstigter'].replace('DKB VISACARD', 'Bar', inplace=True)
# ### Inverse Dataframe to have first day first
# In[34]:
data = data.iloc[::-1]
# In[35]:
fr = data.index[0]
to = data.index[-1]
# ### Cumulative Sum over values plus starting balance
# In[36]:
data['Kontostand (EUR)'] = data['Betrag (EUR)'].cumsum()+kst
# ### Linear Trend
# In[37]:
from scipy.stats import linregress
def fit_line(x, y):
"""Return slope, intercept of best fit line."""
slope, intercept, r, p, stderr = linregress(x, y)
return slope, intercept
# In[38]:
slope, intercept = fit_line( (data.index - data.index[0]).days, data['Kontostand (EUR)'].values)
# In[39]:
print('You gain %.2fEUR per day.' % slope)
# In[40]:
data['Kontostand (EUR) linear'] = (data.index - data.index[0]).days * slope + intercept
# In[41]:
data[['Kontostand (EUR)', 'Kontostand (EUR) linear']].plot(title='Kontostand (DKB #%s)' % knr, figsize=(10,4))
# ### Broke or a Million on
# In[42]:
from datetime import timedelta
if slope < 0:
brokedate = data.index[0] + timedelta(days=((0.0-intercept) / slope))
print('Pleite am %i.%i.%i' % (brokedate.day, brokedate.month, brokedate.year))
else:
try:
goal = 1000000.0 # million
milliondate = data.index[0] + timedelta(days=((goal-intercept) / slope))
print(u'Millionär am %i.%i.%i' % (milliondate.day, milliondate.month, milliondate.year))
except:
print(u'Wahrscheinlich wirst du niemals Millionär!')
# ## Who's doin' it?
# In[43]:
empfgroup = data.groupby(u'Auftraggeber / Begünstigter')['Betrag (EUR)'].sum()
# In[44]:
empfgroup.sort()
# In[45]:
# Short the names
empfgroup.index = [str(i)[:30] for i in empfgroup.index]
# In[46]:
empfgroup.plot(kind='barh', figsize=(5,15),
color=['g' if empfgroup.values[i]>0 else 'r' for i in range(len(empfgroup.values))],
title=u'Aggregierte Zahlungen (%i.%i.%i - %i.%i.%i)' % (fr.day, fr.month, fr.year, to.day, to.month, to.year))
# In[ ]:
# In[ ]: