In [1]:
%matplotlib inline
In [14]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
sns.set_style('darkgrid')
sns.set_context('poster')
In [10]:
df = pd.read_csv('../fixtures/accounts.csv')
df['Month'] = pd.to_datetime(df['Month'])
In [23]:
df[df['Account Type'] == 'Loans']
Out[23]:
Month
Account Type
Bank
Account Name
Beginning Balance
Ending Balance
14
2012-04-03
Loans
Nationstar
Mortgage
-589515.71
-588687.36
15
2012-04-03
Loans
USAA
My Auto Loan
-13196.71
-12633.75
16
2012-04-03
Loans
ACS
Consolidation Loan
-6461.17
-6277.36
17
2012-04-03
Loans
ACS
Federal Stafford Loan
-1690.61
-1528.94
32
2012-05-01
Loans
Nationstar
Mortgage
-588687.36
-587855.84
33
2012-05-01
Loans
USAA
My Auto Loan
-12657.92
-12094.96
34
2012-05-01
Loans
ACS
Consolidation Loan
-11660.88
-11477.06
35
2012-05-01
Loans
ACS
Federal Stafford Loan
-1532.11
-1370.46
50
2012-06-05
Loans
Nationstar
Mortgage
-590417.22
-587021.84
51
2012-06-05
Loans
USAA
My Auto Loan
-12123.89
-11560.93
52
2012-06-05
Loans
ACS
Consolidation Loan
0.00
0.00
53
2012-06-05
Loans
ACS
Federal Stafford Loan
0.00
0.00
69
2012-07-05
Loans
Nationstar
Mortgage
585000.00
-585000.00
70
2012-07-05
Loans
USAA
My Auto Loan
-11584.57
-11021.61
71
2012-07-05
Loans
ACS
Consolidation Loan
-1214.67
-1053.01
72
2012-07-05
Loans
ACS
Federal Stafford Loan
-5973.06
-5789.25
87
2012-08-06
Loans
Wells Fargo
Mortgage
-594763.17
-591544.68
88
2012-08-06
Loans
USAA
My Auto Loan
-11045.62
-10482.66
89
2012-08-06
Loans
ACS
Consolidation Loan
-1055.36
-893.71
90
2012-08-06
Loans
ACS
Federal Stafford Loan
-5808.86
-5625.06
105
2012-09-04
Loans
Nationstar
Mortgage
-591554.68
-588336.21
106
2012-09-04
Loans
USAA
My Auto Loan
-10501.18
-9938.21
107
2012-09-04
Loans
ACS
Consolidation Loan
-895.91
-734.26
108
2012-09-04
Loans
ACS
Federal Stafford Loan
-5645.41
-5461.59
123
2012-10-03
Loans
Nationstar
Mortgage
-590659.58
-587441.11
124
2012-10-03
Loans
USAA
My Auto Loan
-9958.66
-9395.69
125
2012-10-03
Loans
ACS
Consolidation Loan
0.00
0.00
126
2012-10-03
Loans
ACS
Federal Stafford Loan
0.00
0.00
141
2012-11-05
Loans
Nationstar
Mortgage
-589761.68
-586543.21
142
2012-11-05
Loans
USAA
My Auto Loan
-9416.31
-8853.34
...
...
...
...
...
...
...
755
2015-05-01
Loans
ACS
Federal Stafford Loan
0.00
0.00
756
2015-05-01
Loans
USAA
Home Fixup Loan
-2229.11
-2102.42
773
2015-06-01
Loans
Nationstar
Mortgage
-566521.66
-563557.14
774
2015-06-01
Loans
ACS
Consolidation Loan
-1217.86
-1034.03
775
2015-06-01
Loans
ACS
Federal Stafford Loan
0.00
0.00
776
2015-06-01
Loans
USAA
Home Fixup Loan
-2121.69
-1995.00
793
2015-07-02
Loans
Nationstar
Mortgage
-565569.91
-562605.39
794
2015-07-02
Loans
ACS
Consolidation Loan
-1036.44
-852.62
795
2015-07-02
Loans
ACS
Federal Stafford Loan
0.00
0.00
796
2015-07-02
Loans
USAA
Home Fixup Loan
-2011.02
-1884.33
813
2015-08-04
Loans
Nationstar
Mortgage
-564615.57
-561651.06
814
2015-08-04
Loans
ACS
Consolidation Loan
-856.48
-672.66
815
2015-08-04
Loans
ACS
Federal Stafford Loan
0.00
0.00
816
2015-08-04
Loans
USAA
Home Fixup Loan
-1901.59
-1774.91
833
2015-09-03
Loans
Nationstar
Mortgage
-563658.67
-560694.16
834
2015-09-03
Loans
ACS
Consolidation Loan
-672.12
-488.31
835
2015-09-03
Loans
ACS
Federal Stafford Loan
0.00
0.00
836
2015-09-03
Loans
USAA
Home Fixup Loan
-1789.66
-1662.96
853
2015-10-02
Loans
Nationstar
Mortgage
-562699.17
-559734.66
854
2015-10-02
Loans
ACS
Consolidation Loan
-489.47
-305.66
855
2015-10-02
Loans
ACS
Federal Stafford Loan
0.00
0.00
856
2015-10-02
Loans
USAA
Home Fixup Loan
-1676.34
-1549.66
873
2015-11-01
Loans
Nationstar
Mortgage
-561737.06
-558772.54
874
2015-11-01
Loans
ACS
Consolidation Loan
-307.19
0.00
875
2015-11-01
Loans
ACS
Federal Stafford Loan
0.00
0.00
876
2015-11-01
Loans
USAA
Home Fixup Loan
-1562.63
-1435.94
893
2015-12-02
Loans
Nationstar
Mortgage
-560772.36
-557807.83
894
2015-12-02
Loans
ACS
Consolidation Loan
0.00
0.00
895
2015-12-02
Loans
ACS
Federal Stafford Loan
0.00
0.00
896
2015-12-02
Loans
USAA
Home Fixup Loan
-1448.39
-1321.71
208 rows × 6 columns
In [26]:
atypes = df['Account Type'].unique()
data = df[df['Account Type'] == atypes[1]]
sns.tsplot(time="Month", value="Beginning Balance", unit="Account Type", condition="Account Name",data=data)
Out[26]:
<matplotlib.axes._subplots.AxesSubplot at 0x108e64e90>
In [32]:
anames = df['Account Name'].unique()
for idx, name in enumerate(anames): print "{}: {}".format(idx, name)
data = df[df['Account Name'] == anames[20]]
sns.tsplot(time="Month", value="Beginning Balance", unit="Account Type", condition="Account Name",data=data)
0: EveryDay Checking
1: Share Savings
2: Four Star Checking
3: Performance First Savings
4: Bank Account
5: TNS Simple IRA
6: Balanced Long Term MF
7: Roth IRA
8: 401k
9: World Mastercard
10: Discover Card
11: United Visa
12: Amazon Visa
13: nRewards Visa
14: Mortgage
15: My Auto Loan
16: Consolidation Loan
17: Federal Stafford Loan
18: Tactical ETF
19: Home Fixup Loan
20: Exchange Tactical Fund
21: Youth Savings
22: CB&T Roth IRA
23: Google Wallet
Out[32]:
<matplotlib.axes._subplots.AxesSubplot at 0x107644910>
Content source: bbengfort/financial-analysis
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