In this notebook, we're going to work with dates.

Let's get started!

Get our environment set up

The first thing we'll need to do is load in the libraries and dataset we'll be using. We'll be working with a dataset that contains information on landslides that occured between 2007 and 2016. In the following exercise, you'll apply your new skills to a dataset of worldwide earthquakes.

# modules we'll use
import pandas as pd
import numpy as np
import seaborn as sns
import datetime

# read in our data
landslides = pd.read_csv("../input/landslide-events/catalog.csv")

# set seed for reproducibility
np.random.seed(0)

Now we're ready to look at some dates!

Check the data type of our date column

We begin by taking a look at the first five rows of the data.

landslides.head()

	id	date	time	continent_code	country_name	country_code	state/province	population	city/town	distance	...	geolocation	hazard_type	landslide_type	landslide_size	trigger	storm_name	injuries	fatalities	source_name	source_link
0	34	3/2/07	Night	NaN	United States	US	Virginia	16000	Cherry Hill	3.40765	...	(38.600900000000003, -77.268199999999993)	Landslide	Landslide	Small	Rain	NaN	NaN	NaN	NBC 4 news	http://www.nbc4.com/news/11186871/detail.html
1	42	3/22/07	NaN	NaN	United States	US	Ohio	17288	New Philadelphia	3.33522	...	(40.517499999999998, -81.430499999999995)	Landslide	Landslide	Small	Rain	NaN	NaN	NaN	Canton Rep.com	http://www.cantonrep.com/index.php?ID=345054&C...
2	56	4/6/07	NaN	NaN	United States	US	Pennsylvania	15930	Wilkinsburg	2.91977	...	(40.4377, -79.915999999999997)	Landslide	Landslide	Small	Rain	NaN	NaN	NaN	The Pittsburgh Channel.com	https://web.archive.org/web/20080423132842/htt...
3	59	4/14/07	NaN	NaN	Canada	CA	Quebec	42786	Châteauguay	2.98682	...	(45.322600000000001, -73.777100000000004)	Landslide	Riverbank collapse	Small	Rain	NaN	NaN	NaN	Le Soleil	http://www.hebdos.net/lsc/edition162007/articl...
4	61	4/15/07	NaN	NaN	United States	US	Kentucky	6903	Pikeville	5.66542	...	(37.432499999999997, -82.493099999999998)	Landslide	Landslide	Small	Downpour	NaN	NaN	0.0	Matthew Crawford (KGS)	NaN

5 rows × 23 columns

We'll be working with the "date" column from the landslides dataframe. Let's make sure it actually looks like it contains dates.

# print the first few rows of the date column
print(landslides['date'].head())

0     3/2/07
1    3/22/07
2     4/6/07
3    4/14/07
4    4/15/07
Name: date, dtype: object

Yep, those are dates! But just because I, a human, can tell that these are dates doesn't mean that Python knows that they're dates. Notice that at the bottom of the output of head(), you can see that it says that the data type of this column is "object".

Pandas uses the "object" dtype for storing various types of data types, but most often when you see a column with the dtype "object" it will have strings in it.

If you check the pandas dtype documentation here, you'll notice that there's also a specific datetime64 dtypes. Because the dtype of our column is object rather than datetime64, we can tell that Python doesn't know that this column contains dates.

We can also look at just the dtype of a column without printing the first few rows:

# check the data type of our date column
landslides['date'].dtype

dtype('O')

You may have to check the numpy documentation to match the letter code to the dtype of the object. "O" is the code for "object", so we can see that these two methods give us the same information.

Convert our date columns to datetime

Now that we know that our date column isn't being recognized as a date, it's time to convert it so that it is recognized as a date. This is called "parsing dates" because we're taking in a string and identifying its component parts.

We can pandas what the format of our dates are with a guide called as "strftime directive", which you can find more information on at this link. The basic idea is that you need to point out which parts of the date are where and what punctuation is between them. There are lots of possible parts of a date, but the most common are %d for day, %m for month, %y for a two-digit year and %Y for a four digit year.

Some examples:

• 1/17/07 has the format "%m/%d/%y"
• 17-1-2007 has the format "%d-%m-%Y"

Looking back up at the head of the "date" column in the landslides dataset, we can see that it's in the format "month/day/two-digit year", so we can use the same syntax as the first example to parse in our dates:

# create a new column, date_parsed, with the parsed dates
landslides['date_parsed'] = pd.to_datetime(landslides['date'], format="%m/%d/%y")

Now when I check the first few rows of the new column, I can see that the dtype is datetime64. I can also see that my dates have been slightly rearranged so that they fit the default order datetime objects (year-month-day).

# print the first few rows
landslides['date_parsed'].head()

0   2007-03-02
1   2007-03-22
2   2007-04-06
3   2007-04-14
4   2007-04-15
Name: date_parsed, dtype: datetime64[ns]

Now that our dates are parsed correctly, we can interact with them in useful ways.

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• What if I run into an error with multiple date formats? While we're specifying the date format here, sometimes you'll run into an error when there are multiple date formats in a single column. If that happens, you have have pandas try to infer what the right date format should be. You can do that like so:

landslides['date_parsed'] = pd.to_datetime(landslides['Date'], infer_datetime_format=True)

• Why don't you always use infer_datetime_format = True? There are two big reasons not to always have pandas guess the time format. The first is that pandas won't always been able to figure out the correct date format, especially if someone has gotten creative with data entry. The second is that it's much slower than specifying the exact format of the dates.

Select the day of the month

Now that we have a column of parsed dates, we can extract information like the day of the month that a landslide occurred.

# get the day of the month from the date_parsed column
day_of_month_landslides = landslides['date_parsed'].dt.day
day_of_month_landslides.head()

0     2.0
1    22.0
2     6.0
3    14.0
4    15.0
Name: date_parsed, dtype: float64

If we tried to get the same information from the original "date" column, we would get an error: AttributeError: Can only use .dt accessor with datetimelike values. This is because dt.day doesn't know how to deal with a column with the dtype "object". Even though our dataframe has dates in it, we have to parse them before we can interact with them in a useful way.

Plot the day of the month to check the date parsing

One of the biggest dangers in parsing dates is mixing up the months and days. The to_datetime() function does have very helpful error messages, but it doesn't hurt to double-check that the days of the month we've extracted make sense.

To do this, let's plot a histogram of the days of the month. We expect it to have values between 1 and 31 and, since there's no reason to suppose the landslides are more common on some days of the month than others, a relatively even distribution. (With a dip on 31 because not all months have 31 days.) Let's see if that's the case:

# remove na's
day_of_month_landslides = day_of_month_landslides.dropna()

# plot the day of the month
sns.distplot(day_of_month_landslides, kde=False, bins=31)

/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='date_parsed'>

Yep, it looks like we did parse our dates correctly & this graph makes good sense to me.

Your turn

Write code to parse the dates in a dataset of worldwide earthquakes.

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