Pandas deposits and withdrawals over a time period with n-number of people - python-3.x

Question

I'm trying to dynamically build a format in which I want to display number of deposits compared to withdrawals in a timeline chart. Whenever a deposit is done, the graph will go up, and when a withdrawal is done the graph goes down.
This is how far I've gotten:
df.head()
name Deposits Withdrawals
Peter 2019-03-07 2019-03-11
Peter 2019-03-08 2019-03-19
Peter 2019-03-12 2019-05-22
Peter 2019-03-12 2019-10-31
Peter 2019-03-14 2019-04-05
Here is the data manipulation to show the net movements for one person; Peter.
x = pd.Series(df.groupby('Deposits').size())
y = pd.Series(df.groupby('Withdrawals').size())
balance = pd.DataFrame({'net_mov': x.sub(y, fill_value=0)})
balance = balance.assign(Peter=balance.net_mov.cumsum())
print(balance)
net_mov Peter
2019-03-07 1 1
2019-03-08 1 2
2019-03-11 -1 1
2019-03-12 2 3
2019-03-14 1 4
This works perfectly fine, and this is the format that I want to have. Now let's say I want to extend on this and not just list Peters deposits and withdrawals, but I want to add n-number of people. Lets assume that my dataframe looks like this:
df2.head()
name Deposits Withdrawals
Peter 2019-03-07 2019-03-11
Anna 2019-03-08 2019-03-19
Anna 2019-03-12 2019-05-22
Peter 2019-03-12 2019-10-31
Simon 2019-03-14 2019-04-05
The format I'm aiming for is this. I don't know how to group everything, and I don't know which names or how many columns there will be beforehand, so I can't hardcode names or number of columns. It has to be generate dynamically.
net_mov1 Peter net_mov2 Anna net_mov3 Simon
2019-03-07 1 1 1 1 2 2
2019-03-08 1 2 2 3 -1 1
2019-03-11 -1 1 0 3 2 3
2019-03-12 2 3 -2 1 4 7
2019-03-14 1 4 3 4 -1 6
UPDATE:
First off, thanks for the help. I'm getting closer to my goal. This is the progress:
x = pd.Series(df.groupby(['Created', 'name']).size())
y = pd.Series(df.groupby(['Finished', 'name']).size())
balance = pd.DataFrame({'net_mov': x.sub(y, fill_value=0)})
balance = balance.assign(balance=balance.groupby('name').net_mov.cumsum())
balance_byname = balance.groupby('name')
balance_byname.get_group("Peter")
Output:
net_mov balance
name Created Finished
Peter 2017-07-03 2017-07-06 1 1
2017-07-10 1 2
2017-07-13 0 2
2017-07-14 1 3
... ... ...
2020-07-29 2020-07-15 0 4581
2020-07-17 0 4581
2020-07-20 0 4581
2020-07-21 -1 4580
[399750 rows x 2 columns]
This is of course too many rows, the dataset I'm working with has around 2500 rows.
I've tried to unstack it but that creates problems on it's own.

Answer 1

Given df:
name Deposits Withdrawals
Peter 2019-03-07 2019-03-11
Anna 2019-03-08 2019-03-19
Anna 2019-03-12 2019-05-22
Peter 2019-03-12 2019-10-31
Simon 2019-03-14 2019-04-05
You can melt dataframe, indicate deposits by 1 and withdravals by -1, and then pivot:
df = pd.DataFrame(\
{'name': {0: 'Peter', 1: 'Anna', 2: 'Anna', 3: 'Peter', 4: 'Simon'},
'Deposits': {0: '2019-03-07',
1: '2019-03-08',
2: '2019-03-12',
3: '2019-03-12',
4: '2019-03-14'},
'Withdrawals': {0: '2019-03-11',
1: '2019-03-19',
2: '2019-05-22',
3: '2019-10-31',
4: '2019-04-05'}})
df2 = df.melt('name')\
.assign(variable = lambda x: x.variable.map({'Deposits':1,'Withdrawals':-1}))\
#.pivot('value','name','variable').fillna(0)\
#use pivot_table with sum aggregate, because there may be duplicates in data
.pivot_table('variable','value','name', aggfunc = 'sum').fillna(0)\
.rename(columns = lambda c: f'{c} netmov' )
Above will give net change of balance:
name Anna netmov Peter netmov Simon netmov
value
2019-03-07 0.0 1.0 0.0
2019-03-08 1.0 0.0 0.0
2019-03-11 0.0 -1.0 0.0
2019-03-12 1.0 1.0 0.0
2019-03-14 0.0 0.0 1.0
2019-03-19 -1.0 0.0 0.0
2019-04-05 0.0 0.0 -1.0
2019-05-22 -1.0 0.0 0.0
2019-10-31 0.0 -1.0 0.0
Finally calculate balance using cumulative sum and concatenate it with previously calculated net changes:
df2 = pd.concat([df2,df2.cumsum().rename(columns = lambda c: c.split()[0] + ' balance')], axis = 1)\
.sort_index(axis=1)
result:
name Anna balance Anna netmov ... Simon balance Simon netmov
value ...
2019-03-07 0.0 0.0 ... 0.0 0.0
2019-03-08 1.0 1.0 ... 0.0 0.0
2019-03-11 1.0 0.0 ... 0.0 0.0
2019-03-12 2.0 1.0 ... 0.0 0.0
2019-03-14 2.0 0.0 ... 1.0 1.0
2019-03-19 1.0 -1.0 ... 1.0 0.0
2019-04-05 1.0 0.0 ... 0.0 -1.0
2019-05-22 0.0 -1.0 ... 0.0 0.0
2019-10-31 0.0 0.0 ... 0.0 0.0
[9 rows x 6 columns]

Answer 2

Try making use of pandas MultiIndex. This is almost the same code copied from your question BUT
including the column name into the groupby argument
adding a .groupby('name') call in the last line
With the code:
x = pd.Series(df.groupby(['Deposits', 'name']).size())
y = pd.Series(df.groupby(['Withdrawals', 'name']).size())
balance = pd.DataFrame({'net_mov': x.sub(y, fill_value=0)})
balance = balance.assign(balance=balance.groupby('name').net_mov.cumsum())
The groupby in the lastline effectively tells pandas to treat each name as a separate dataframe before applying cumsum, so movements will be kept to each account.
Now you can keep it in this shape with only two columns and the name as a second level in the rows MultiIndex. You can set a groupby object by calling
balance_byname = balance.groupby('name') # notice there is no aggregation nor transformation
To be used whenever you need to access only one account with .get_group() https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.GroupBy.get_group.html#pandas.core.groupby.GroupBy.get_group
OR---
You can also add a new line at the end
balance = balance.unstack('name')
Which will give a shape similar to what you ask in the expected output. This will, however, possibly create a number of 'NaN' by having all dates by all names. This can drastically increase the memory usage IF there are many dates and many bames, with each name having movements only in a few dates.