Using python to plot ledger transactions

ledger is a very capable piece of software for double-entry accounting. However, it only provides one out-of-the-box option for visualizing transactions: gnuplot. Rather than learn a non-FOSS tool (gnuplot is not really GNU), I set out to get ledger transactions into pandas. ledger includes python bindings which worked for 95% of my plotting needs, but some missing journal properties were ultimately a showstopper. Fortunately, the gnuplot flags (-J and -j) produce output almost ready to be parsed by pandas.

Using python-ledger

The python bindings are pleasant to use, but require some work to get into pandas.

# Necessary imports
import matplotlib.pyplot as plt  
import ledger  
import pandas as pd

# Read ledger journal
journal = ledger.read_journal("/data/ledger/finances.ldg")  

Queries are performed to find transactions after the journal is read.

# Find all Income transactions
posts = journal.query("Income")  

However, posts is not easily digestible by pandas. It is possible to build a parseable list by iterating through each transaction:

income = []  
for post in journal.query("Income"):  
    trans = {'Income':post.amount,'Datetime'}
incomeDF = pd.DataFrame(income)  

The current incomeDF has a standard index (1,2,...X) and the Datetime column.

Datetime Income
0 2016-11-04 $-115.23
1 2016-12-01 $-144.04
... ... ...
543 2017-04-24 $-100.00

The standard index can be replaced with Datetime allowing Grouper methods to be used.

incomeDF = incomeDF.set_index(pd.DatetimeIndex(incomeDF['Datetime']))  
2016-11-04 $-115.23
2016-12-01 $-144.04
... ...
2017-04-24 $-100.00

The Income data is a string and cannot be plotted with pandas. The '$' can be removed and the new string cast as a float.

incomeDf['Income'] = incomeDF['Income'].replace('[\$]','',regex=True).astype(float)  

With the transactions in a dataframe anything is possible.

Since the index is Datetime the transactions can be grouped by month and summed.

monthly = df.groupby(pd.TimeGrouper(freq='M'))  
monthly = monthly.sum().ix['2016-06-01':'2016-12-31'].fillna(0).abs()  

Creating a dataframe for "Expenses" and combining it with incomeDF
(pd.concat([incomeDF,expensesDF],join='outer')) allows a plot comparing Income and Expenses to be made.'fivethirtyeight')  
ax = monthly.loc[:,['Income','Expenses']]  
ax.set_xticklabels([dt.strftime('%b') for dt in monthly.index.to_pydatetime()])  
plt.title('Income and Expenses Comparison')  
rate = 1-(monthly['Expenses'])/monthly['Income']  
ax2 = ax.twinx()  
ax.legend(loc='center left',bbox_to_anchor=(1.1,0.5))  
ax.set_ylabel('Amount ($)')  
ax2.set_ylabel('Savings Rate (%)', color='g')  

 Python Plot

Using gnuplot output

The -j and -J flags cause ledger to produce gnuplot friendly output. Fortunately, this output is straightforward: a date column and a transaction column. The -J provides a running total while -j produces each individual transaction amount.

2017-01-13 144.04  
2017-01-13 1438.31  
2017-01-13 450  

Calling the ledger binary from python is accomplished using the subprocess module.

import subprocess as sp  
import io  
import pandas as pd

# Run ledger using subprocess
ledger_file = '/data/ledger/finances.ldg'  
result ='ledger -f {} -JMRV reg Assets'.format(ledger_file),shell=True,stdout=sp.PIPE)  
output = result.stdout.decode('ascii')  

The results from are binary encoded and must be decoded to ASCII for python string operations to work.

Pandas' read_csv expects to read a file. Fortunately, io provides a way for an in-memory string to be treated as a file with StringIO.

# 'Fake' csv
f = io.StringIO(output)

# Create Dataframe
df = pd.read_csv(f,delim_whitespace=True,names=['Date','Amount'],parse_dates={'datetime':['Date']},index_col='datetime')  

The transactions are trivial to plot now that they are stored in a dataframe.


gnuplot plot

Was this information useful or thought provoking? Do you appreciate a webpage free of analytics or ads? Say thanks and help keep this site online by using my Amazon Affilliate URL . I'll receive a small percentage of purchases made within 24 hours of clicking.