This question already has answers here:
How to filter by month, day, year with Pandas
(1 answer)
Keep only date part when using pandas.to_datetime
(13 answers)
Closed 4 years ago.
Why didn't my date filter work? All others filters work fine.
import pandas as pd
import datetime
data =pd.DataFrame({
'country': ['USA', 'USA', 'Belarus','Brazil'],
'time': ['2018-01-15 16:11:45.923570+00:00', '2018-01-15 16:19:45.923570+00:00', '2018-01-16 16:12:45.923570+00:00', '2018-01-17 16:14:45.923570+00:00']})
# Конвертируем в datetime
data['time'] = pd.to_datetime(data['time'])
# Конвертируем в date
data['time'] = data['time'].dt.date
print(data)
# Ищем дату '2018-12-12'
select_date = data.loc[data['time'] == '2018-01-17']
print(select_date)
How can I filter exact data from dataframe?
How can I iterate dataframe by date daily?
for i in data:
All rows in a specific day
I wish you all good luck and prosperity!
datetime.date objects are not vectorised with Pandas. The docs indicate this:
Returns numpy array of python datetime.date objects
Regular Python objects are stored in object dtype series which do not support fancy date indexing. Instead, you can normalize:
data['time'] = pd.to_datetime(data['time'])
select_date = data.loc[data['time'].dt.normalize() == '2018-01-17']
You can use the same idea to iterate your dataframe by day:
for day, day_df in data.groupby(data['time'].dt.normalize()):
# do something with day_df
Related
This question already has answers here:
Select DataFrame rows between two dates
(13 answers)
Closed 10 months ago.
I have a Pandas data frame that look like the following:
df = pd.DataFrame({'foo' : ['spam', 'ham', 'eggs', 'spam'],
'timestamp' : ['2022-04-20 15:03:05.325618982-04:00',
'2022-04-19 19:22:43.569068909-04:00',
'2022-04-18 06:38:28.928778887-04:00',
'2022-04-15 21:04:28.928778887-04:00']
})
The timestamp column is a datetime object, which is created using the following:
df['timestamp'] = df['timestamp'].dt.tz_localize('GMT').dt.tz_convert('America/New_York')
I'd like to subset the df such that only the rows between the start_date and end_date range are returned.
I tried the following:
start_date = '2022-04-18 00:00:00.000000000'
end_date = '2022-04-19 00:00:00.000000000'
df = df[df['timestamp'].isin(pd.date_range(start_date, end_date))]
But, this results in an empty dataframe.
How would I do the subset by defining start_date and end_date only in YYYY-MM-DD format?
Thank you!
You can check between
out = df[df['timestamp'].between(start_date,end_date)]
Out[219]:
foo timestamp
2 eggs 2022-04-18 06:38:28.928778887-04:00
This question already has answers here:
Extract day and month from a datetime object
(4 answers)
Closed 12 months ago.
I recently started using python.
I have a series of dates in excel
01-05-2021
02-05-2021
.
.
29-05-2021
Now, I want to load this column and convert it into individual strings based on rows. So i can extract the day, month and year separately for each dates
Can someone help me how to do that??
you can do:
df = pd.read_excel("filename.xlsx")
# let's imagine your date column name is "date"
df["day"] = df["date"].apply(lambda elem:elem.split("-")[0])
df["month"] = df["date"].apply(lambda elem:elem.split("-")[1])
df["year"] = df["date"].apply(lambda elem:elem.split("-")[2])
from datetime import datetime
str_time = 01-05-2021
time_convert = datetime.strptime(str_time, '%d-%m-%Y')
print (time_convert, time_convert.day, time_convert.month, time_convert.year)
in your case, make the convert in looping for each data you got from the excel file
This question already has answers here:
Select DataFrame rows between two dates
(13 answers)
Closed 2 months ago.
I have one dataset with several columns: id, Unixtime, X, Y, etc....
Unixtime is one sequence of date: 01-01-2010 00:00:00, 02-01-2010 00:00:00...etc up to 31-12-2021 23:59:59.
I would like to get data within specific range, between 01-01-2019 00:00:00 until 31-12-2019 23:59:59.
I tried with this script but I have had some problem, because I am not sure if python take all data.
import pandas as pd
data = pd.read_csv('name.csv', sep=';')
data.info()
start_time = '01-01-2010 00:00:00'
end_time = '31-12-2019 23:59:59'
mask =(data['Unixtime']>start_time)&(data['Unixtime']<=end_time]
x=data.loc[mask]
There is another solution?
f_date = data [(data['Unixtime Date'] < '23-03-21') & (data['Unixtime Date'] > '03-03-21')]
This question already has answers here:
Pandas filter dataframe rows with a specific year
(2 answers)
Closed 3 years ago.
I have a date column in a data frame that looks like this:
(Year-Month-Day)
2017-09-21
2018-11-25
I am trying to create a function that considers only the year, I have been trying the following.
df[df['DateColumn'].str[:3]=='2017']
But I am receiving this error:
AttributeError: Can only use .str accessor with string values, which use np.object_ dtype in pandas
How can I only consider the first four characters of the date in a function? Thanks.
I think you are looking for:
df['year'] = [d.year for d in df['DateColumn']]
This works only if the elements of the column are pandas.tslib.Timestamp. If not then :
df['DateColumn'] = pd.to_datetime(df['DateColumn'])
df['year'] = [d.year for d in df['DateColumn']]
UPDATE: Use this instead:
df.loc[pd.to_datetime(df['DateColumn']).dt.year == 2017]
According to this:
https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#dt-accessor
If you have a Series in a DateTime format, you should be able to use the dt accessor.
So you might be able to do something like this:
df[df.dt.year == 2017]
Try:
df = pd.to_datetime(df.col).apply(lambda x: x.year)
This converts col into datetime format, then extracts year from it to make it a series.
This question already has answers here:
Add a sequential counter column on groups to a pandas dataframe
(4 answers)
Closed 4 years ago.
if I have a data set of time series and I want to estimate the number of the day of a groupby time series per each day as seen in the figure and act as a counter :
nothing special in my code yet, it is just reading the data and convert time and day into
import pandas as pd
df = pd.read_csv('*file location and name*',sep=",")
df.head()
df['Date'] =pd.to_datetime(df['Date']+" "+df['Time'])
df.set_index('Date', inplace=True)
See if answers your query:
df['dayOfMonth']= df.groupby('day').cumcount() + 1