I am working on a real estate cash-flow simulation.
What I want in the end is a time series where everyday I report if the property is vacant, leased and if I collected rent.
In my present code, I create first a profit array with values of "Leased", "Vacant" or "Today you collected rent of $1000", so I used this to create my time series:
rng=pd.date_range('6/1/2016', periods=len(profit), freq='D')
ts=pd.Series(profit, index=rng)
To simplify, I assumed I collected rent every 30 days. Now I want to be more specific and collect it every 5th day of the month (for example) and be flexible on the day the next tenant will move in.
Do you know commands or a good source where I can learn how to iterate from month to month?
Any help would be appreciated
You can build a sequence of dates using date_range and .shift() (freq='M' is for month-end frequencies) with pd.datetools.day like so:
date_sequence = pd.date_range(start, end, freq='M').shift(num_of_days, freq=pd.datetools.day)
and then use this sequence to select dates from the DateTimeIndex using
df.loc[date_sequence, 'column_name'] = value
Alternatively, you can use pd.DateOffset() like so:
ts = pd.date_range(start=date(2015, 6, 1), end=date(2015, 12, 1), freq='MS')
DatetimeIndex(['2015-06-01', '2015-07-01', '2015-08-01', '2015-09-01',
'2015-10-01', '2015-11-01', '2015-12-01'],
dtype='datetime64[ns]', freq='MS')
Now add 5 days:
ts + pd.DateOffset(days=5)
to get:
DatetimeIndex(['2015-06-06', '2015-07-06', '2015-08-06', '2015-09-06',
'2015-10-06', '2015-11-06', '2015-12-06'],
dtype='datetime64[ns]', freq=None)
Related
I have a good sheet that I want to grab the header which a date time stamp which will match against another sheet find the entries with that date and suburb and type and give me an average cost.
My formula is =AVERAGEIFS(Sheet1!C:C,Sheet1!A:A, B11:B, Sheet1!F:F, C10) which gives me the average but i've hard coded the header date:
example:
What I want to do is dynamically add the data from the row above with the date time instead of of manually adding it in the formula something like this:
=AVERAGEIFS(Sheet1!C:C,Sheet1!A:A, B11:B, Sheet1!F:F, =CHAR(COLUMN()+64) & 10)
Which would automatically grab the column + row 10 e.g C10, D10, E10.
If i put =CHAR(COLUMN()+64) & 10 in its own cell it works but when I add it to averageifs condition it gives me a parsing error.
Expecting C10, D10, E10 from =CHAR(COLUMN()+64) & 10 which should allow me to dynamically filter data on the date int he header above it.
try:
=AVERAGEIFS(Sheet1!C:C, Sheet1!A:A, B11:B, Sheet1!F:F, INDIRECT(CHAR(COLUMN()+64)&10))
I have a water flowmeter connected to a RPi which is writing data to a simple RRD
RRDs::create ($rrdfile, "--start", 1572829200,
"--step", 60,
"DS:FLOW1:GAUGE:90:U:U",
"RRA:MAX:0.5:1:10512000",);
From this I generate a graph for the last 24 hours and some statistics for the last few days. A simplified version follows
RRDs::graph "temps.png",
"--start=now-1d",
"--end=now",
"--width=1000",
"--base=1000",
"--height=240",
"--title=Flow Data - ",
"--slope-mode",
"--vertical-label=Volume of Water",
"DEF:flow-now=flow.rrd:FLOW1:AVERAGE", #Used to generate the graph
"DEF:flow-1d=flow.rrd:FLOW1:AVERAGE:end=midnight:start=end-1d", #Data for yesterday
"CDEF:flow-1d-1=flow-1d,25440,/", #Convert raw data to litres
"VDEF:flow-1dtotal=flow-1d-1,TOTAL", #Get total litres
"GPRINT:flow-1dtotal:Total Volume last 1 day = %.2lf L", #Print total for yesterday
I would like to add an arbitrary value to flow-1dtotal but can't work out how. Something along the lines of the psuedo code below is what I need
flow-1dtotal = flow-1dtotal + 1000
Thanks for reading and for any suggestions
So I have a dataframe that I dump a time series into. The index is the date. I need to do calculations based on date.
For eg. I have {
XRT_Close
Date
2010-01-04 35.94
2010-01-05 36.17
2010-01-06 36.50
...
2015-02-07 36.60
2015-02-08 36.52 }
How would I go about doing say... Percentage change of beginning to end of the month? How would I construct a loop to cycle through the months?
Any help will be met with huge appreciation. Thank you.
First create year and month columns:
df['year'] = [x.year for x in df.index]
df['month'] = [x.month for x in df.index]
Group by them:
grouped = df.groupby(['year','month'])
Define the function you want to run on the groups:
def PChange(df):
begin = df['column_name'].iloc[0]
end = df['column_name'].iloc[-1]
return (end-begin)/(end+begin)*100
Apply the function to the groups:
grouped.apply(PChange)
Let me know if it works.
Since I didn't find anywhere else this topic I will ask it here. I am getting data from CSV file, I have written datetime format in one of columns. I get that column with pandas module and then I need to count occurrences in specific time slots and plot that with matplotlib. Bellow you can see example of column.
Time and Date
0 2015-08-21 10:51:06.398000
1 2015-08-21 10:51:00.017000
2 2015-08-21 10:52:06.402000
3 2015-08-21 10:54:06.407000
...
I know how I can split time like so:
pd.date_range("10:50", "12:30", freq="1min").time
But how can I assign occurrences of my read values from CSV and then plot it? Any advice or direction would help.
It's hard to tell what you want as you haven't posted desired output but if I understand you correctly you want to count the number of rows in time intervals of certain length. You can do this by combining resample and len. To use resample, first set the index to 'Time and Date:
df.set_index('Date and Time', drop=False)
Note that drop=False is only necessary if the data frame has no other columns.
Then to get the number of rows in each 1-minute interval do
counts = df.resample('1min', len).astype(int)
If there are multiple dates and you want to sum the counts for each time interval over dates do
counts.groupby(lambda ts: ts.time()).sum()
Is there any way I can easily check if a string conforms to the SortableDateTimePattern ("s"), or do I need to write a regular expression?
I've got a form where users can input a copyright date (as a string), and these are the allowed formats:
Year: YYYY (eg 1997)
Year and month: YYYY-MM (eg 1997-07)
Complete date: YYYY-MM-DD (eg 1997-07-16)
Complete date plus hours and minutes: YYYY-MM-DDThh:mmTZD (eg 1997-07-16T19:20+01:00)
Complete date plus hours, minutes and seconds: YYYY-MM-DDThh:mm:ssTZD (eg 1997-07-16T19:20:30+01:00)
Complete date plus hours, minutes, seconds and a decimal fraction of a second
YYYY-MM-DDThh:mm:ss.sTZD (eg 1997-07-16T19:20:30.45+01:00)
I don't have much experience of writing regular expressions so if there's an easier way of doing it I'd be very grateful!
Not thoroughly tested and hence not foolproof, but the following seems to work:
var regex:RegExp = /(?<=\s|^)\d{4}(-\d{2}(-\d{2}(T\d{2}:\d{2}(:\d{2}(\.\d{2})?)?\+\d{2}:\d{2})?)?)?(?=\s|$)/g;
var test:String = "23 1997 1998-07 1995-07s 1937-04-16 " +
"1970-0716 1993-07-16T19:20+01:01 1979-07-16T19:20+0100 " +
"2997-07-16T19:20:30+01:08 3997-07-16T19:20:30.45+01:00";
var result:Object
while(result = regex.exec(test))
trace(result[0]);
Traced output:
1997
1998-07
1937-04-16
1993-07-16T19:20+01:01
2997-07-16T19:20:30+01:08
3997-07-16T19:20:30.45+01:00
I am using ActionScript here, but the regex should work in most flavors. When implementing it in your language, note that the first and last / are delimiters and the last g stands for global.
I'd split the input field into many (one for year, month, day etc.).
You can use Javscript to advance from one field to the next once full (i.e. once four characters are in the year box, move focus to month) for smoother entry.
You can then validate each field independently and finally construct the complete date string.