I have a pd.Series object with a pd.DatetimeIndex containing dates. I would like to calculate the difference from a past value, for example the one month before. The values are not exactly aligned to the months, so I cannot simply add a monthly date offset. There might also be missing data.
So I would like to match the previous value using an offset and a tolerance. One way to do this is using the .reindex() method with method='nearest' which matches the previous data point almost like I want to:
shifted = data.copy()
shifted.index = shifted.index + pd.DateOffset(months=1)
shifted = shifted.reindex(
data.index,
method="nearest",
tolerance=timedelta(days=100),
)
return data - shifted
Here we calculate the difference from the value one month before, but we tolerate finding a value 100 days around that timestamp.
This is almost what I want, but I want to avoid subtracting the value from itself. I always want to subtract a value in the past, or no value at all.
For example: if this is the data
2020-01-02 1.0
2020-02-03 2.0
2020-04-05 3.0
And I use the code above, the last data point, 3.0 will be subtracted from itself, since its date is closer to 2020-05-05 than to 2020-03-03. And the result will be
2020-01-02 0.0
2020-02-03 1.0
2020-04-05 0.0
While the goal is to get
2020-01-02 NaN
2020-02-03 1.0
2020-04-05 1.0
Additional edit after Baron Legendre's answer (thanks for pointing out the flaw in my question):
The tolerance variable is also important to me. So let's say there is a gap of a year in the data, that falls outside the tolerance of 100 days, and the result should be NaN:
2015-12-04 10.0
2020-01-02 1.0
2020-02-03 2.0
2020-04-05 3.0
Should result in:
2015-12-05 NaN (because there is no past value to subtract)
2020-01-02 NaN (because the past value is too far back)
2020-02-03 1.0
2020-04-05 1.0
Hope that explains the problem well enough. Any ideas on how to do this efficiently, without looping over every single data point?
ser
###
2015-12-04 10
2020-01-02 1
2020-02-03 2
2020-04-05 3
dtype: int64
df = ser.reset_index()
tdiff = df['index'].diff().dt.days
ser[:] = np.where(tdiff > 100, np.nan, ser - ser.shift())
ser
###
2015-12-04 NaN
2020-01-02 NaN
2020-02-03 1.0
2020-04-05 1.0
dtype: float64
Related
I have a dataframe similar to the one below.
dry_bulb_tmp_mean Time Timedelta
Date
2011-01-14 -11.245833 2011-01-14 NaN
2011-01-15 -12.608333 2011-01-15 1.0
2011-01-16 -15.700000 2011-01-16 1.0
2011-01-17 -19.954167 2011-01-17 1.0
2011-01-20 -13.654167 2011-01-20 3.0
2011-01-21 -11.887500 2011-01-21 1.0
2011-01-22 -17.866667 2011-01-22 1.0
2011-01-23 -23.250000 2011-01-23 1.0
2011-01-24 -23.654167 2011-01-24 1.0
2011-01-25 -16.254167 2011-01-25 1.0
2011-01-30 -12.233333 2011-01-30 5.0
2011-01-31 -19.041667 2011-01-31 1.0
I am tasked with creating a new dataframe that gives me the lengths for different runs. Basically, a run is however many consecutive days occur in the dataframe. For example, from the 14th, to the 17th I get a run of 4, but then at the 20th I get a run of 1. I have attempted to do this as follows.
if temp_persis33['Timedelta'].iloc[row] == 1:
length += 1
Every time a value greater than 1 is found in the Timedelta column, it will append the counter to a list, and then reset the counter. However, I am not sure how to compare the values in the dataframe. I have tried a few different things and nothing has worked. Any help would be appreciated, thank you.
IIUC try grouping on a boolean array where Timedelta does not equal 1
df.groupby(df['Timedelta'].ne(1).cumsum())['Time'].count().to_numpy()
# array([4, 6, 2])
You don't even need to create the Timedelta column. You can apply diff directly to it. pd.DataFrame.diff() works directly with datetime:
df.groupby(df.Time.diff().ne(1).cumsum()).Time.count()
I am having some trouble managing and combining columns in order to get one datetime column out of three columns containing the date, the hours and the minutes.
Assume the following df (copy and type df= = pd.read_clipboard() to reproduce) with the types as noted below:
>>>df
date hour minute
0 2021-01-01 7.0 15.0
1 2021-01-02 3.0 30.0
2 2021-01-02 NaN NaN
3 2021-01-03 9.0 0.0
4 2021-01-04 4.0 45.0
>>>df.dtypes
date object
hour float64
minute float64
dtype: object
I want to replace the three columns with one called 'datetime' and I have tried a few things but I face the following problems:
I first create a 'time' column df['time']= (pd.to_datetime(df['hour'], unit='h') + pd.to_timedelta(df['minute'], unit='m')).dt.time and then I try to concatenate it with the 'date' df['datetime']= df['date'] + ' ' + df['time'] (with the purpose of converting the 'datetime' column pd.to_datetime(df['datetime']). However, I get
TypeError: can only concatenate str (not "datetime.time") to str
If I convert 'hour' and 'minute' to str to concatenate the three columns to 'datetime', then I face the problem with the NaN values, which prevents me from converting the 'datetime' to the corresponding type.
I have also tried to first convert the 'date' column df['date']= df['date'].astype('datetime64[ns]') and again create the 'time' column df['time']= (pd.to_datetime(df['hour'], unit='h') + pd.to_timedelta(df['minute'], unit='m')).dt.time to combine the two: df['datetime']= pd.datetime.combine(df['date'],df['time']) and it returns
TypeError: combine() argument 1 must be datetime.date, not Series
along with the warning
FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.
Is there a generic solution to combine the three columns and ignore the NaN values (assume it could return 00:00:00).
What if I have a row with all NaN values? Would it possible to ignore all NaNs and 'datetime' be NaN for this row?
Thank you in advance, ^_^
First convert date to datetimes and then add hour and minutes timedeltas with replace missing values to 0 timedelta:
td = pd.Timedelta(0)
df['datetime'] = (pd.to_datetime(df['date']) +
pd.to_timedelta(df['hour'], unit='h').fillna(td) +
pd.to_timedelta(df['minute'], unit='m').fillna(td))
print (df)
date hour minute datetime
0 2021-01-01 7.0 15.0 2021-01-01 07:15:00
1 2021-01-02 3.0 30.0 2021-01-02 03:30:00
2 2021-01-02 NaN NaN 2021-01-02 00:00:00
3 2021-01-03 9.0 0.0 2021-01-03 09:00:00
4 2021-01-04 4.0 45.0 2021-01-04 04:45:00
Or you can use Series.add with fill_value=0:
df['datetime'] = (pd.to_datetime(df['date'])
.add(pd.to_timedelta(df['hour'], unit='h'), fill_value=0)
.add(pd.to_timedelta(df['minute'], unit='m'), fill_value=0))
I would recommend converting hour and minute columns to string and constructing the datetime string from the provided components.
Logically, you need to perform the following steps:
Step 1. Fill missing values for hour and minute with zeros.
df['hour'] = df['hour'].fillna(0)
df['minute'] = df['minute'].fillna(0)
Step 2. Convert float values for hour and minute into integer ones, because your final output should look like 2021-01-01 7:15, not 2021-01-01 7.0:15.0.
df['hour'] = df['hour'].astype(int)
df['minute'] = df['minute'].astype(int)
Step 3. Convert integer values for hour and minute to the string representation.
df['hour'] = df['hour'].astype(str)
df['minute'] = df['minute'].astype(str)
Step 4. Concatenate date, hour and minute into one column of the correct format.
df['result'] = df['date'].str.cat(df['hour'].str.cat(df['minute'], sep=':'), sep=' ')
Step 5. Convert your result column to datetime object.
pd.to_datetime(df['result'])
It is also possible to fullfill all of this steps in one command, though it will read a bit messy:
df['result'] = pd.to_datetime(df['date'].str.cat(df['hour'].fillna(0).astype(int).astype(str).str.cat(df['minute'].fillna(0).astype(int).astype(str), sep=':'), sep=' '))
Result:
date hour minute result
0 2020-01-01 7.0 15.0 2020-01-01 07:15:00
1 2020-01-02 3.0 30.0 2020-01-02 03:30:00
2 2020-01-02 NaN NaN 2020-01-02 00:00:00
3 2020-01-03 9.0 0.0 2020-01-03 09:00:00
4 2020-01-04 4.0 45.0 2020-01-04 04:45:00
I am working with UPC (product#), date_expected, and quantity_picked columns and need my data organized to show the total quantity_picked per day (for every day) for each UPC. Example data below:
UPC quantity_picked date_expected
0 0001111041660 1.0 2019-05-14 15:00:00
1 0001111045045 1.0 2019-05-14 15:00:00
2 0001111050268 1.0 2019-05-14 15:00:00
3 0001111086132 1.0 2019-05-14 15:00:00
4 0001111086983 1.0 2019-05-14 15:00:00
5 0001111086984 1.0 2019-05-14 15:00:00
... ... ...
39694 0004470036000 6.0 2019-06-24 20:00:00
39695 0007225001116 1.0 2019-06-24 20:00:00
I was able to successfully organize my data in this manner using the code below, but the output leaves out dates with quantity_picked=0
orders = pd.read_sql_query(SQL, con=sql_conn)
order_daily = orders.copy()
order_daily['date_expected'] = order_daily['date_expected'].dt.normalize()
order_daily['date_expected'] = pd.to_datetime(order_daily.date_expected, format='%Y-%m-%d')
# Groups by date and UPC getting the sum of quanitity picked for each
# then resets index to fill in dates for all rows
tipd = order_daily.groupby(['UPC', 'date_expected']).sum().reset_index()
# Rearranging of columns to put UPC column first
tipd = tipd[['UPC','date_expected','quantity_picked']]
gives the following output:
UPC date_expected quantity_picked
0 0000000002554 2019-05-21 4.0
1 0000000002554 2019-05-24 2.0
2 0000000002554 2019-06-02 2.0
3 0000000002554 2019-06-17 2.0
4 0000000003082 2019-05-15 2.0
5 0000000003082 2019-05-16 2.0
6 0000000003082 2019-05-17 8.0
... ... ...
31588 0360600051715 2019-06-17 1.0
31589 0501072452748 2019-06-15 1.0
31590 0880100551750 2019-06-07 2.0
When I try to follow the solution given in:
Pandas filling missing dates and values within group
I adjust my code to
tipd = order_daily.groupby(['UPC', 'date_expected']).sum().reindex(idx, fill_value=0).reset_index()
# Rearranging of columns to put UPC column first
tipd = tipd[['UPC','date_expected','quantity_picked']]
# Viewing first 10 rows to check format of dataframe
print('Preview of Total per Item per Day')
print(tipd.iloc[0:10])
And receive the following error:
TypeError: Argument 'tuples' has incorrect type (expected numpy.ndarray, got DatetimeArray)
I need each date to be listed for each product, even when quantity picked is zero. I plan on creating two new columns using .shift and .diff for calculations, and those columns will not be accurate if my data is skipping dates.
Any guidance is very much appreciated.
I have a Python Dataframe that looks like this:
Facility PUE PUEraw Servers
2016-11-14 00:00:00 6.0 NaN 1.2 5.0
2016-11-14 00:30:00 6.0 NaN 1.2 5.0
2016-11-14 01:00:00 6.0 NaN 1.2 5.0
etc.
As you can see, the index is date/time. The dataframe is updated with a new value every half hour.
I'm trying to write a script that removes all rows except those that correspond to TODAY's date, for which I am utilising date = dt.datetime.today(). However, I am struggling, partly perhaps because the index also contains the time.
Does anyone have any suggestions? Alternatively, a script that removes all but the last 48 rows would also work for me (the last 48 x half hourly values = the latest day's data).
Here are two options you can use to extract data on a specific day:
df['2016-11-16']
# Facility PUE PUEraw Servers
# 2016-11-16 01:00:00 6.0 NaN 1.2 5.0
import datetime
df[df.index.date == datetime.datetime.today().date()]
# Facility PUE PUEraw Servers
# 2016-11-16 01:00:00 6.0 NaN 1.2 5.0
You can always access the last rows in a DataFrame with df.tail()
df = df.tail(48)
For further information:
Pandas Documentation
I am trying use pandas to resample vessel tracking data from seconds to minutes using how='first'. The dataframe is called hg1s. The unique ID is called MMSI. The datetime index is TX_DTTM. Here is a data sample:
TX_DTTM MMSI LAT LON NS
2013-10-01 00:00:02 367542760 29.660550 -94.974195 15
2013-10-01 00:00:04 367542760 29.660550 -94.974195 15
2013-10-01 00:00:07 367451120 29.614161 -94.954459 0
2013-10-01 00:00:15 367542760 29.660210 -94.974069 15
2013-10-01 00:00:13 367542760 29.660210 -94.974069 15
The code to resample:
hg1s1min = hg1s.groupby('MMSI').resample('1Min', how='first')
And a data sample of the output:
hg1s1min[20000:20004]
MMSI TX_DTTM NS LAT LON
367448060 2013-10-21 00:42:00 NaN NaN NaN
2013-10-21 00:43:00 NaN NaN NaN
2013-10-21 00:44:00 NaN NaN NaN
2013-10-21 00:45:00 NaN NaN NaN
It's safe to assume that there are several data points within each minute, so I don't understand why this isn't picking up the first record for that method. I looked at this link: Pandas Downsampling Issue because it seemed similar to my problem. I tried passing label='left' and label='right', neither worked.
How do I return the first record in every minute for each MMSI?
As it turns out, the problem isn't with the method, but with my assumption about the data. The large data set is a month, or 44640 minutes. While every record in my dataset has the relevant values, there isn't 100% overlap in time. In this case MMSI = 367448060 is present at 2013-10-17 23:24:31 and again at 2013-10-29 20:57:32. between those two data points, there isn't data to sample, resulting in a NaN, which is correct.