Date Functionality

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来自 University of Michigan 的课程

Introduction to Data Science in Python

10731 个评分

University of Michigan

Introduction to Data Science in Python

10731 个评分

课程 1（共 5 门，Specialization Applied Data Science with Python）

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

从本节课中

Week 3

In this week you'll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We'll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment.

Merging Dataframes7:42

Pandas Idioms6:33

Pivot Tables2:25

Date Functionality5:08

与讲师见面

Christopher Brooks

Welcome to today's tutorial,

where we'll be looking at the time series and date functionally in pandas.

Pandas has four main time related classes.

Timestamp, DatetimeIndex, Period, and PeriodIndex.

First, let's look at Timestamp.

Timestamp represents a single timestamp and associates values with points in time.

For example, let's create a timestamp using a string

9/1/2016 10:05AM, and here we have our timestamp.

Timestamp is interchangeable with Python's datetime in most cases.

Suppose we weren't interested in a specific point in time, and

instead wanted a span of time.

This is where Period comes into play.

Period represents a single time span, such as a specific day or month.

Here we are creating a period that is January 2016,

and here's an example of a period that is March 5th, 2016.

The index of a timestamp is DatetimeIndex.

Let's look at a quick example.

First, let's create our example series t1,

we'll use the Timestamp of September 1st, 2nd and 3rd of 2016.

When we look at the series, each Timestamp is the index and

has a value associated with it, in this case, a, b and c.

Looking at the type of our series index, we see that it's DatetimeIndex.

Similarly, the index of period is PeriodIndex.

Let's create another example series t2.

This time, we'll use the values d, e, and f and

match them with the period September, October and November 2016.

Looking at the type of the ts2.index, we can see that it's PeriodIndex.

Now, let's look into how to convert to Datetime.

Suppose we have a list of dates as strings.

If we create a DataFrame using these dates as the index.

And some randomly generated data, this is the DataFrame we get.

Looking at the index we can see that it’s pretty messy and

the dates are all in different formats.

Using pandas to_datetime, pandas will try to convert these to Datetime and

put them in a standard format.

to_datetime also has options to change the date parse order.

For example, we can pass in the argument dayfirst = True to

parse the date in European date format.

Timedeltas are differences in times.

We can see that when we take the difference between September 3rd and

September 1st,

we get a Timedelta of two days.

We can also do something like find what the date and time is for 12 days and

three hours past September 2nd, at 8:10 AM.

Next, let's look at a few tricks for working with dates in a DataFrame.

Suppose we want to look at nine measurements, taken bi-weekly,

every Sunday,

starting in October 2016.

Using date_range, we can create this DatetimeIndex.

Now, let's create a DataFrame using these dates, and some random data, and

see what we can do with it.

First, we can check what day of the week a specific date is.

For example, here we can see that all the dates in our index are on a Sunday.

We can use diff to find the difference between each date's value.

Suppose we wanted to know what the mean count is for each month in our DataFrame.

We can do this using resample.

We can use partial string indexing to find values from a particular year, or

from a particular month, or we can even slice on a range of dates.

For example, here we only want the values from December 2016 onwards.

Another cool thing we can do is change the frequency of our dates in our DataFrame

using asfreq.

If we use this to change the frequency from bi-weekly to weekly,

we'll end up with missing values every other week.

So let's use the forward fill method on those missing values.

One last thing I wanted to briefly touch upon is plotting time series.

Importing matplotlib.pyplot, and using the iPython magic %mapplotlib inline,

will allow you to visualize the time series in the notebook.

In the next course, we will learn more about understanding and

creating visualizations.

Thanks for watching, see you next time.

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