How do you convert series to time in python?
I have a Pandas DataFrame as below Show
TimeReviewed is a series type
I've tried below, but it still doesn't change the Series type
How can I change the df.TimeReviewed to DateTime type and pull out year, month, day, hour, min, sec separately? I'm kinda new to python, thanks for your help. [image courtesy: https://pixabay.com/]As someone who works with time series data on almost a daily basis, I have found the pandas Python package to be extremely useful for time series manipulation and analysis. This basic introduction to time series data manipulation with pandas should allow you to get started in your time series analysis. Specific objectives are to show you how to:
Let’s get
started. If you want to play with real data that you have, you may want to start by using pandas First import the libraries we’ll be working with and then use them to create a date range import pandas as pd This date range
has timestamps with an hourly frequency. If we call DatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 01:00:00', We can check the type of the first element: type(date_rng[0])#returnspandas._libs.tslib.Timestamp Let’s create an example data frame with the timestamp data and look at the first 15 elements: df = pd.DataFrame(date_rng, columns=['date'])df['data'] = np.random.randint(0,100,size=(len(date_rng)))df.head(15)Example data frame — df If we want to do time series manipulation, we’ll need to have a date time index so that our data frame is indexed on the timestamp. Convert the data frame index to a datetime index then show the first elements: df['datetime'] = pd.to_datetime(df['date'])df = df.set_index('datetime')df.drop(['date'], axis=1, inplace=True)df.head()df with datetime index What if our ‘time’ stamps in our data are actually string type vs. numerical? Let’s convert our string_date_rng = [str(x) for x in date_rng]string_date_rng#returns['2018-01-01 00:00:00', We can convert the strings to timestamps by inferring their format, then look at the values: timestamp_date_rng = pd.to_datetime(string_date_rng, infer_datetime_format=True)timestamp_date_rng#returnsDatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 01:00:00', But what about if we need to convert a unique string format? Let’s create an arbitrary list of dates that are strings and convert them to timestamps: string_date_rng_2 = ['June-01-2018', 'June-02-2018', 'June-03-2018']timestamp_date_rng_2 = [datetime.strptime(x,'%B-%d-%Y') for x in string_date_rng_2]timestamp_date_rng_2#returns[datetime.datetime(2018, 6, 1, 0, 0), What does it look like if we put this into a data frame? df2 = pd.DataFrame(timestamp_date_rng_2, columns=['date'])df2 Going back to our original data frame, let’s look at the data by parsing on timestamp index: Say we just want to see data where the date is the 2nd of the month, we could use the index as per below. df[df.index.day == 2] The top of this looks like: We could also directly call a date that we want to look at via the index of the data frame: df['2018-01-03'] What about selecting data between certain dates? df['2018-01-04':'2018-01-06']
The basic data frame that we’ve populated gives us data on an hourly frequency, but we can resample the data at a different frequency and specify how we would like to compute the summary statistic for the new sample frequency. We could take the min, max, average, sum, etc., of the data at a daily frequency instead of an hourly frequency as per the example below where we compute the daily average of the data: df.resample('D').mean() What about window statistics such as a rolling mean or a rolling sum? Let’s create a new column in our original df that computes the rolling sum over a 3 window period and then look at the top of the data frame: df['rolling_sum'] = df.rolling(3).sum() We can see that this is computing correctly and that it only starts having valid values when there are three periods over which to look back. This is a good chance to see how we can do forward or backfilling of data when working with missing data values. Here’s our df but with a new column that takes the rolling sum and backfills the data: df['rolling_sum_backfilled'] = df['rolling_sum'].fillna(method='backfill') It’s often useful to be able to fill your missing data with realistic values such as the average of a time period, but always remember that if you are working with a time series problem and want your data to be realistic, you should not do a backfill of your data as that’s like looking into the future and getting information you would never have at that time period. Likely you will want to forward fill your data more frequently than you backfill. When working with time series data, you may come across time values that are in Unix time. Unix time, also called Epoch time is the number of seconds that have elapsed since 00:00:00 Coordinated Universal Time (UTC), Thursday, 1 January 1970. Using Unix time helps to disambiguate time stamps so that we don’t get confused by time zones, daylight savings time, etc. Here’s an example of a time t that is in Epoch time and converting unix/epoch time to a regular time stamp in UTC: epoch_t = 1529272655 If I wanted to convert that time that is in UTC to my own time zone, I could simply do the following: real_t.tz_localize('UTC').tz_convert('US/Pacific')#returnsTimestamp('2018-06-17 14:57:35-0700', tz='US/Pacific') With these basics, you should be all set to work with your time series data. Here are a few tips to keep in mind and common pitfalls to avoid when working with time series data:
How do you convert data to time series in Python?To resample time-series data, use the pandas resample() function. It is a time series frequency conversion and resampling convenience technique. The caller must give the label of a DateTime-like series/index to the on/level keyword argument if the object has a DateTime-like index.
How do you convert a series to a DataFrame?In pandas, converting a series to a DataFrame is a straightforward process. pandas uses the to_frame() method to easily convert a series into a data frame.. The passed name should substitute for the series name (if it has one).. The fault is None.. Returns the DataFrame representation of Series.. How do you change the series in Python?Modify Pandas series in place using non-NA values. In [2]: s = pd. Series([2, 3, 4]) s. update(pd. Series([5, 6, 7])) s. ... . In [3]: s = pd. Series(['p', 'q', 'r']) s. update(pd. Series(['s', 't'], index=[0, 2])) s. ... . In [4]: s = pd. Series([2, 3, 4]) s. update(pd. ... . In [5]: s = pd. Series([1, 2, 3]) s. update(pd.. How do you cut time series data?Slicing time series data. import numpy as np import matplotlib.pyplot as plt import pandas as pd from timeseries import read_data Copy. ... . # Load input data index = 2 data = read_data('data_2D.txt', index) Copy.. |