Lesson 3¶

Get Data - Our data set will consist of an Excel file containing customer counts per date. We will learn how to read in the excel file for processing.
Prepare Data - The data is an irregular time series having duplicate dates. We will be challenged in compressing the data and coming up with next years forecasted customer count.
Analyze Data - We use graphs to visualize trends and spot outliers. Some built in computational tools will be used to calculate next years forecasted customer count.
Present Data - The results will be plotted.

NOTE: Make sure you have looked through all previous lessons, as the knowledge learned in previous lessons will be needed for this exercise.

In [1]:
# Import libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy.random as np
import sys
import matplotlib

%matplotlib inline

In [2]:
print('Python version ' + sys.version)
print('Pandas version: ' + pd.__version__)
print('Matplotlib version ' + matplotlib.__version__)

Python version 3.7.4 (default, Aug  9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)]
Pandas version: 1.0.5
Matplotlib version 3.2.2


We will be creating our own test data for analysis.

In [3]:
# set seed
np.seed(111)

# Function to generate test data
def CreateDataSet(Number=1):

Output = []

for i in range(Number):

# Create a weekly (mondays) date range
rng = pd.date_range(start='1/1/2009', end='12/31/2012', freq='W-MON')

# Create random data
data = np.randint(low=25,high=1000,size=len(rng))

# Status pool
status = [1,2,3]

# Make a random list of statuses
random_status = [status[np.randint(low=0,high=len(status))] for i in range(len(rng))]

# State pool
states = ['GA','FL','fl','NY','NJ','TX']

# Make a random list of states
random_states = [states[np.randint(low=0,high=len(states))] for i in range(len(rng))]

Output.extend(zip(random_states, random_status, data, rng))

return Output


Now that we have a function to generate our test data, lets create some data and stick it into a dataframe.

In [4]:
dataset = CreateDataSet(4)
df = pd.DataFrame(data=dataset, columns=['State','Status','CustomerCount','StatusDate'])
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 836 entries, 0 to 835
Data columns (total 4 columns):
#   Column         Non-Null Count  Dtype
---  ------         --------------  -----
0   State          836 non-null    object
1   Status         836 non-null    int64
2   CustomerCount  836 non-null    int64
3   StatusDate     836 non-null    datetime64[ns]
dtypes: datetime64[ns](1), int64(2), object(1)
memory usage: 26.2+ KB

In [5]:
df.head()

Out[5]:
State Status CustomerCount StatusDate
0 GA 1 877 2009-01-05
1 FL 1 901 2009-01-12
2 fl 3 749 2009-01-19
3 FL 3 111 2009-01-26
4 GA 1 300 2009-02-02

We are now going to save this dataframe into an Excel file, to then bring it back to a dataframe. We simply do this to show you how to read and write to Excel files.

We do not write the index values of the dataframe to the Excel file, since they are not meant to be part of our initial test data set.

In [6]:
# Save results to excel
df.to_excel('Lesson3.xlsx', index=False)
print('Done')

Done


Grab Data from Excel¶

We will be using the read_excel function to read in data from an Excel file. The function allows you to read in specfic tabs by name or location.

In [7]:
pd.read_excel?

Signature:
io,
sheet_name=0,
names=None,
index_col=None,
usecols=None,
squeeze=False,
dtype=None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skiprows=None,
nrows=None,
na_values=None,
keep_default_na=True,
verbose=False,
parse_dates=False,
date_parser=None,
thousands=None,
comment=None,
skipfooter=0,
convert_float=True,
mangle_dupe_cols=True,
**kwds,
)
Docstring:
Read an Excel file into a pandas DataFrame.

Supports xls, xlsx, xlsm, xlsb, and odf file extensions
read from a local filesystem or URL. Supports an option to read
a single sheet or a list of sheets.

Parameters
----------
io : str, bytes, ExcelFile, xlrd.Book, path object, or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, and file. For file URLs, a host is
expected. A local file could be: file://localhost/path/to/table.xlsx.

If you want to pass in a path object, pandas accepts any os.PathLike.

By file-like object, we refer to objects with a read() method,
such as a file handler (e.g. via builtin open function)
or StringIO.
sheet_name : str, int, list, or None, default 0
Strings are used for sheet names. Integers are used in zero-indexed
sheet positions. Lists of strings/integers are used to request
multiple sheets. Specify None to get all sheets.

Available cases:

* Defaults to 0: 1st sheet as a DataFrame
* 1: 2nd sheet as a DataFrame
* "Sheet1": Load sheet with name "Sheet1"
* [0, 1, "Sheet5"]: Load first, second and sheet named "Sheet5"
as a dict of DataFrame
* None: All sheets.

header : int, list of int, default 0
Row (0-indexed) to use for the column labels of the parsed
DataFrame. If a list of integers is passed those row positions will
be combined into a MultiIndex. Use None if there is no header.
names : array-like, default None
List of column names to use. If file contains no header row,
then you should explicitly pass header=None.
index_col : int, list of int, default None
Column (0-indexed) to use as the row labels of the DataFrame.
Pass None if there is no such column.  If a list is passed,
those columns will be combined into a MultiIndex.  If a
subset of data is selected with usecols, index_col
is based on the subset.
usecols : int, str, list-like, or callable default None
* If None, then parse all columns.
* If str, then indicates comma separated list of Excel column letters
and column ranges (e.g. "A:E" or "A,C,E:F"). Ranges are inclusive of
both sides.
* If list of int, then indicates list of column numbers to be parsed.
* If list of string, then indicates list of column names to be parsed.

* If callable, then evaluate each column name against it and parse the
column if the callable returns True.

Returns a subset of the columns according to behavior above.

squeeze : bool, default False
If the parsed data only contains one column then return a Series.
dtype : Type name or dict of column -> type, default None
Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
Use object to preserve data as stored in Excel and not interpret dtype.
If converters are specified, they will be applied INSTEAD
of dtype conversion.
engine : str, default None
If io is not a buffer or path, this must be set to identify io.
Acceptable values are None, "xlrd", "openpyxl" or "odf".
converters : dict, default None
Dict of functions for converting values in certain columns. Keys can
either be integers or column labels, values are functions that take one
input argument, the Excel cell content, and return the transformed
content.
true_values : list, default None
Values to consider as True.
false_values : list, default None
Values to consider as False.
skiprows : list-like
Rows to skip at the beginning (0-indexed).
nrows : int, default None
Number of rows to parse.

na_values : scalar, str, list-like, or dict, default None
Additional strings to recognize as NA/NaN. If dict passed, specific
per-column NA values. By default the following values are interpreted
as NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',
'1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a',
'nan', 'null'.
keep_default_na : bool, default True
Whether or not to include the default NaN values when parsing the data.
Depending on whether na_values is passed in, the behavior is as follows:

* If keep_default_na is True, and na_values are specified, na_values
is appended to the default NaN values used for parsing.
* If keep_default_na is True, and na_values are not specified, only
the default NaN values are used for parsing.
* If keep_default_na is False, and na_values are specified, only
the NaN values specified na_values are used for parsing.
* If keep_default_na is False, and na_values are not specified, no
strings will be parsed as NaN.

Note that if na_filter is passed in as False, the keep_default_na and
na_values parameters will be ignored.
na_filter : bool, default True
Detect missing value markers (empty strings and the value of na_values). In
data without any NAs, passing na_filter=False can improve the performance
verbose : bool, default False
Indicate number of NA values placed in non-numeric columns.
parse_dates : bool, list-like, or dict, default False
The behavior is as follows:

* bool. If True -> try parsing the index.
* list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
each as a separate date column.
* list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
a single date column.
* dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call
result 'foo'

If a column or index contains an unparseable date, the entire column or
index will be returned unaltered as an object data type. If you dont want to
parse some cells as date just change their type in Excel to "Text".
For non-standard datetime parsing, use pd.to_datetime after pd.read_excel.

Note: A fast-path exists for iso8601-formatted dates.
date_parser : function, optional
Function to use for converting a sequence of string columns to an array of
datetime instances. The default uses dateutil.parser.parser to do the
conversion. Pandas will try to call date_parser in three different ways,
advancing to the next if an exception occurs: 1) Pass one or more arrays
(as defined by parse_dates) as arguments; 2) concatenate (row-wise) the
string values from the columns defined by parse_dates into a single array
and pass that; and 3) call date_parser once for each row using one or
more strings (corresponding to the columns defined by parse_dates) as
arguments.
thousands : str, default None
Thousands separator for parsing string columns to numeric.  Note that
this parameter is only necessary for columns stored as TEXT in Excel,
any numeric columns will automatically be parsed, regardless of display
format.
comment : str, default None
Comments out remainder of line. Pass a character or characters to this
argument to indicate comments in the input file. Any data between the
comment string and the end of the current line is ignored.
skipfooter : int, default 0
Rows at the end to skip (0-indexed).
convert_float : bool, default True
Convert integral floats to int (i.e., 1.0 --> 1). If False, all numeric
data will be read in as floats: Excel stores all numbers as floats
internally.
mangle_dupe_cols : bool, default True
Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
'X'...'X'. Passing in False will cause data to be overwritten if there
are duplicate names in the columns.
**kwds : optional
Optional keyword arguments can be passed to TextFileReader.

Returns
-------
DataFrame or dict of DataFrames
DataFrame from the passed in Excel file. See notes in sheet_name
argument for more information on when a dict of DataFrames is returned.

--------
to_excel : Write DataFrame to an Excel file.
to_csv : Write DataFrame to a comma-separated values (csv) file.

Examples
--------
The file can be read using the file name as string or an open file object:

>>> pd.read_excel('tmp.xlsx', index_col=0)  # doctest: +SKIP
Name  Value
0   string1      1
1   string2      2
2  #Comment      3

...               sheet_name='Sheet3')  # doctest: +SKIP
Unnamed: 0      Name  Value
0           0   string1      1
1           1   string2      2
2           2  #Comment      3

Index and header can be specified via the index_col and header arguments

0         1      2
0  NaN      Name  Value
1  0.0   string1      1
2  1.0   string2      2
3  2.0  #Comment      3

Column types are inferred but can be explicitly specified

...               dtype={'Name': str, 'Value': float})  # doctest: +SKIP
Name  Value
0   string1    1.0
1   string2    2.0
2  #Comment    3.0

True, False, and NA values, and thousands separators have defaults,
but can be explicitly specified, too. Supply the values you would like
as strings or lists of strings!

...               na_values=['string1', 'string2'])  # doctest: +SKIP
Name  Value
0       NaN      1
1       NaN      2
2  #Comment      3

Comment lines in the excel input file can be skipped using the comment kwarg

>>> pd.read_excel('tmp.xlsx', index_col=0, comment='#')  # doctest: +SKIP
Name  Value
0  string1    1.0
1  string2    2.0
2     None    NaN
File:      c:\users\17862\anaconda3\lib\site-packages\pandas\io\excel\_base.py
Type:      function


Note: The location on the Excel file will be in the same folder as the notebook, unless specified otherwise.

In [8]:
# Location of file
Location = r'C:\notebooks\pandas\Lesson3.xlsx'

# Parse a specific sheet
df.dtypes

Out[8]:
State            object
Status            int64
CustomerCount     int64
dtype: object
In [9]:
df.index

Out[9]:
DatetimeIndex(['2009-01-05', '2009-01-12', '2009-01-19', '2009-01-26',
'2009-02-02', '2009-02-09', '2009-02-16', '2009-02-23',
'2009-03-02', '2009-03-09',
...
'2012-10-29', '2012-11-05', '2012-11-12', '2012-11-19',
'2012-11-26', '2012-12-03', '2012-12-10', '2012-12-17',
'2012-12-24', '2012-12-31'],
dtype='datetime64[ns]', name='StatusDate', length=836, freq=None)
In [10]:
df.head()

Out[10]:
State Status CustomerCount
StatusDate
2009-01-05 GA 1 877
2009-01-12 FL 1 901
2009-01-19 fl 3 749
2009-01-26 FL 3 111
2009-02-02 GA 1 300

Prepare Data¶

This section attempts to clean up the data for analysis.

1. Make sure the state column is all in upper case
2. Only select records where the account status is equal to "1"
3. Merge (NJ and NY) to NY in the state column
4. Remove any outliers (any odd results in the data set)

Lets take a quick look on how some of the State values are upper case and some are lower case

In [11]:
df['State'].unique()

Out[11]:
array(['GA', 'FL', 'fl', 'TX', 'NY', 'NJ'], dtype=object)

To convert all the State values to upper case we will use the upper() function and the dataframe's apply attribute. The lambda function simply will apply the upper function to each value in the State column.

In [12]:
# Clean State Column, convert to upper case
df['State'] = df.State.apply(lambda x: x.upper())

In [13]:
df['State'].unique()

Out[13]:
array(['GA', 'FL', 'TX', 'NY', 'NJ'], dtype=object)
In [14]:
# Only grab where Status == 1


To turn the NJ states to NY we simply...

[df.State == 'NJ'] - Find all records in the State column where they are equal to NJ.
df.State[df.State == 'NJ'] = 'NY' - For all records in the State column where they are equal to NJ, replace them with NY.

In [15]:
# Convert NJ to NY


Now we can see we have a much cleaner data set to work with.

In [16]:
df['State'].unique()

Out[16]:
array(['GA', 'FL', 'NY', 'TX'], dtype=object)

At this point we may want to graph the data to check for any outliers or inconsistencies in the data. We will be using the plot() attribute of the dataframe.

As you can see from the graph below it is not very conclusive and is probably a sign that we need to perform some more data preparation.

In [17]:
df['CustomerCount'].plot(figsize=(15,5));


If we take a look at the data, we begin to realize that there are multiple values for the same State, StatusDate, and Status combination. It is possible that this means the data you are working with is dirty/bad/inaccurate, but we will assume otherwise. We can assume this data set is a subset of a bigger data set and if we simply add the values in the CustomerCount column per State, StatusDate, and Status we will get the Total Customer Count per day.

In [18]:
sortdf = df[df['State']=='NY'].sort_index(axis=0)

Out[18]:
State Status CustomerCount
StatusDate
2009-01-19 NY 1 522
2009-02-23 NY 1 710
2009-03-09 NY 1 992
2009-03-16 NY 1 355
2009-03-23 NY 1 728
2009-03-30 NY 1 863
2009-04-13 NY 1 520
2009-04-20 NY 1 820
2009-04-20 NY 1 937
2009-04-27 NY 1 447

Our task is now to create a new dataframe that compresses the data so we have daily customer counts per State and StatusDate. We can ignore the Status column since all the values in this column are of value 1. To accomplish this we will use the dataframe's functions groupby and sum().

Note that we had to use reset_index . If we did not, we would not have been able to group by both the State and the StatusDate since the groupby function expects only columns as inputs. The reset_index function will bring the index StatusDate back to a column in the dataframe.

In [19]:
# Group by State and StatusDate
Daily = df.reset_index().groupby(['State','StatusDate']).sum()

Out[19]:
Status CustomerCount
State StatusDate
FL 2009-01-12 1 901
2009-02-02 1 653
2009-03-23 1 752
2009-04-06 2 1086
2009-06-08 1 649

The State and StatusDate columns are automatically placed in the index of the Daily dataframe. You can think of the index as the primary key of a database table but without the constraint of having unique values. Columns in the index as you will see allow us to easily select, plot, and perform calculations on the data.

Below we delete the Status column since it is all equal to one and no longer necessary.

In [20]:
del Daily['Status']

Out[20]:
CustomerCount
State StatusDate
FL 2009-01-12 901
2009-02-02 653
2009-03-23 752
2009-04-06 1086
2009-06-08 649
In [21]:
# What is the index of the dataframe
Daily.index

Out[21]:
MultiIndex([('FL', '2009-01-12'),
('FL', '2009-02-02'),
('FL', '2009-03-23'),
('FL', '2009-04-06'),
('FL', '2009-06-08'),
('FL', '2009-07-06'),
('FL', '2009-07-13'),
('FL', '2009-07-20'),
('FL', '2009-08-10'),
('FL', '2009-08-24'),
...
('TX', '2012-01-09'),
('TX', '2012-02-27'),
('TX', '2012-03-12'),
('TX', '2012-04-23'),
('TX', '2012-04-30'),
('TX', '2012-08-06'),
('TX', '2012-08-20'),
('TX', '2012-08-27'),
('TX', '2012-09-03'),
('TX', '2012-10-29')],
names=['State', 'StatusDate'], length=239)
In [22]:
# Select the State index
Daily.index.levels[0]

Out[22]:
Index(['FL', 'GA', 'NY', 'TX'], dtype='object', name='State')
In [23]:
# Select the StatusDate index
Daily.index.levels[1]

Out[23]:
DatetimeIndex(['2009-01-05', '2009-01-12', '2009-01-19', '2009-02-02',
'2009-02-23', '2009-03-09', '2009-03-16', '2009-03-23',
'2009-03-30', '2009-04-06',
...
'2012-09-24', '2012-10-01', '2012-10-08', '2012-10-22',
'2012-10-29', '2012-11-05', '2012-11-12', '2012-11-19',
'2012-11-26', '2012-12-10'],
dtype='datetime64[ns]', name='StatusDate', length=161, freq=None)

Lets now plot the data per State.

As you can see by breaking the graph up by the State column we have a much clearer picture on how the data looks like. Can you spot any outliers?

In [24]:
Daily.loc['FL'].plot()
Daily.loc['GA'].plot()
Daily.loc['NY'].plot()
Daily.loc['TX'].plot();


We can also just plot the data on a specific date, like 2012. We can now clearly see that the data for these states is all over the place. since the data consist of weekly customer counts, the variability of the data seems suspect. For this tutorial we will assume bad data and proceed.

In [25]:
Daily.loc['FL']['2012':].plot()
Daily.loc['GA']['2012':].plot()
Daily.loc['NY']['2012':].plot()
Daily.loc['TX']['2012':].plot();


We will assume that per month the customer count should remain relatively steady. Any data outside a specific range in that month will be removed from the data set. The final result should have smooth graphs with no spikes.

StateYearMonth - Here we group by State, Year of StatusDate, and Month of StatusDate.
Daily['Outlier'] - A boolean (True or False) value letting us know if the value in the CustomerCount column is ouside the acceptable range.

We will be using the attribute transform instead of apply. The reason is that transform will keep the shape(# of rows and columns) of the dataframe the same and apply will not. By looking at the previous graphs, we can realize they are not resembling a gaussian distribution, this means we cannot use summary statistics like the mean and stDev. We use percentiles instead. Note that we run the risk of eliminating good data.

In [26]:
# Calculate Outliers
StateYearMonth = Daily.groupby([Daily.index.get_level_values(0), Daily.index.get_level_values(1).year, Daily.index.get_level_values(1).month])
Daily['Lower'] = StateYearMonth['CustomerCount'].transform( lambda x: x.quantile(q=.25) - (1.5*x.quantile(q=.75)-x.quantile(q=.25)) )
Daily['Upper'] = StateYearMonth['CustomerCount'].transform( lambda x: x.quantile(q=.75) + (1.5*x.quantile(q=.75)-x.quantile(q=.25)) )
Daily['Outlier'] = (Daily['CustomerCount'] < Daily['Lower']) | (Daily['CustomerCount'] > Daily['Upper'])

# Remove Outliers
Daily = Daily[Daily['Outlier'] == False]


The dataframe named Daily will hold customer counts that have been aggregated per day. The original data (df) has multiple records per day. We are left with a data set that is indexed by both the state and the StatusDate. The Outlier column should be equal to False signifying that the record is not an outlier.

In [27]:
Daily.head()

Out[27]:
CustomerCount Lower Upper Outlier
State StatusDate
FL 2009-01-12 901 450.5 1351.5 False
2009-02-02 653 326.5 979.5 False
2009-03-23 752 376.0 1128.0 False
2009-04-06 1086 543.0 1629.0 False
2009-06-08 649 324.5 973.5 False

We create a separate dataframe named ALL which groups the Daily dataframe by StatusDate. We are essentially getting rid of the State column. The Max column represents the maximum customer count per month. The Max column is used to smooth out the graph.

In [28]:
# Combine all markets

# Get the max customer count by Date
ALL = pd.DataFrame(Daily['CustomerCount'].groupby(Daily.index.get_level_values(1)).sum())
ALL.columns = ['CustomerCount'] # rename column

# Group by Year and Month
YearMonth = ALL.groupby([lambda x: x.year, lambda x: x.month])

# What is the max customer count per Year and Month
ALL['Max'] = YearMonth['CustomerCount'].transform(lambda x: x.max())

Out[28]:
CustomerCount Max
StatusDate
2009-01-05 877 901
2009-01-12 901 901
2009-01-19 522 901
2009-02-02 953 953
2009-02-23 710 953

As you can see from the ALL dataframe above, in the month of January 2009, the maximum customer count was 901. If we had used apply, we would have got a dataframe with (Year and Month) as the index and just the Max column with the value of 901.

There is also an interest to gauge if the current customer counts were reaching certain goals the company had established. The task here is to visually show if the current customer counts are meeting the goals listed below. We will call the goals BHAG (Big Hairy Annual Goal).

• 12/31/2011 - 1,000 customers
• 12/31/2012 - 2,000 customers
• 12/31/2013 - 3,000 customers

We will be using the date_range function to create our dates.

Definition: date_range(start=None, end=None, periods=None, freq='D', tz=None, normalize=False, name=None, closed=None)
Docstring: Return a fixed frequency datetime index, with day (calendar) as the default frequency

By choosing the frequency to be A or annual we will be able to get the three target dates from above.

In [29]:
pd.date_range?

Signature:
pd.date_range(
start=None,
end=None,
periods=None,
freq=None,
tz=None,
normalize=False,
name=None,
closed=None,
**kwargs,
) -> pandas.core.indexes.datetimes.DatetimeIndex
Docstring:
Return a fixed frequency DatetimeIndex.

Parameters
----------
start : str or datetime-like, optional
Left bound for generating dates.
end : str or datetime-like, optional
Right bound for generating dates.
periods : int, optional
Number of periods to generate.
freq : str or DateOffset, default 'D'
Frequency strings can have multiples, e.g. '5H'. See
:ref:here <timeseries.offset_aliases> for a list of
frequency aliases.
tz : str or tzinfo, optional
Time zone name for returning localized DatetimeIndex, for example
'Asia/Hong_Kong'. By default, the resulting DatetimeIndex is
timezone-naive.
normalize : bool, default False
Normalize start/end dates to midnight before generating date range.
name : str, default None
Name of the resulting DatetimeIndex.
closed : {None, 'left', 'right'}, optional
Make the interval closed with respect to the given frequency to
the 'left', 'right', or both sides (None, the default).
**kwargs
For compatibility. Has no effect on the result.

Returns
-------
rng : DatetimeIndex

--------
DatetimeIndex : An immutable container for datetimes.
timedelta_range : Return a fixed frequency TimedeltaIndex.
period_range : Return a fixed frequency PeriodIndex.
interval_range : Return a fixed frequency IntervalIndex.

Notes
-----
Of the four parameters start, end, periods, and freq,
exactly three must be specified. If freq is omitted, the resulting
DatetimeIndex will have periods linearly spaced elements between
start and end (closed on both sides).

To learn more about the frequency strings, please see this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>__.

Examples
--------
**Specifying the values**

The next four examples generate the same DatetimeIndex, but vary
the combination of start, end and periods.

Specify start and end, with the default daily frequency.

>>> pd.date_range(start='1/1/2018', end='1/08/2018')
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
'2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
dtype='datetime64[ns]', freq='D')

Specify start and periods, the number of periods (days).

>>> pd.date_range(start='1/1/2018', periods=8)
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
'2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
dtype='datetime64[ns]', freq='D')

Specify end and periods, the number of periods (days).

>>> pd.date_range(end='1/1/2018', periods=8)
DatetimeIndex(['2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28',
'2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'],
dtype='datetime64[ns]', freq='D')

Specify start, end, and periods; the frequency is generated
automatically (linearly spaced).

>>> pd.date_range(start='2018-04-24', end='2018-04-27', periods=3)
DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00',
'2018-04-27 00:00:00'],
dtype='datetime64[ns]', freq=None)

**Other Parameters**

Changed the freq (frequency) to 'M' (month end frequency).

>>> pd.date_range(start='1/1/2018', periods=5, freq='M')
DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
'2018-05-31'],
dtype='datetime64[ns]', freq='M')

Multiples are allowed

>>> pd.date_range(start='1/1/2018', periods=5, freq='3M')
DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
'2019-01-31'],
dtype='datetime64[ns]', freq='3M')

freq can also be specified as an Offset object.

>>> pd.date_range(start='1/1/2018', periods=5, freq=pd.offsets.MonthEnd(3))
DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
'2019-01-31'],
dtype='datetime64[ns]', freq='3M')

Specify tz to set the timezone.

>>> pd.date_range(start='1/1/2018', periods=5, tz='Asia/Tokyo')
DatetimeIndex(['2018-01-01 00:00:00+09:00', '2018-01-02 00:00:00+09:00',
'2018-01-03 00:00:00+09:00', '2018-01-04 00:00:00+09:00',
'2018-01-05 00:00:00+09:00'],
dtype='datetime64[ns, Asia/Tokyo]', freq='D')

closed controls whether to include start and end that are on the
boundary. The default includes boundary points on either end.

>>> pd.date_range(start='2017-01-01', end='2017-01-04', closed=None)
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'],
dtype='datetime64[ns]', freq='D')

Use closed='left' to exclude end if it falls on the boundary.

>>> pd.date_range(start='2017-01-01', end='2017-01-04', closed='left')
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'],
dtype='datetime64[ns]', freq='D')

Use closed='right' to exclude start if it falls on the boundary.

>>> pd.date_range(start='2017-01-01', end='2017-01-04', closed='right')
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'],
dtype='datetime64[ns]', freq='D')
File:      c:\users\17862\anaconda3\lib\site-packages\pandas\core\indexes\datetimes.py
Type:      function

In [30]:
# Create the BHAG dataframe
data = [1000,2000,3000]
idx = pd.date_range(start='12/31/2011', end='12/31/2013', freq='A')
BHAG = pd.DataFrame(data, index=idx, columns=['BHAG'])
BHAG

Out[30]:
BHAG
2011-12-31 1000
2012-12-31 2000
2013-12-31 3000

Combining dataframes as we have learned in previous lesson is made simple using the concat function. Remember when we choose axis = 0 we are appending row wise.

In [31]:
# Combine the BHAG and the ALL data set
combined = pd.concat([ALL,BHAG], axis=0)
combined = combined.sort_index(axis=0)
combined.tail()

Out[31]:
CustomerCount Max BHAG
2012-11-19 136.0 1115.0 NaN
2012-11-26 1115.0 1115.0 NaN
2012-12-10 1269.0 1269.0 NaN
2012-12-31 NaN NaN 2000.0
2013-12-31 NaN NaN 3000.0
In [32]:
fig, axes = plt.subplots(figsize=(12, 7))

combined['Max'].plot(color='blue', label='All Markets')
plt.legend(loc='best');


There was also a need to forecast next year's customer count and we can do this in a couple of simple steps. We will first group the combined dataframe by Year and place the maximum customer count for that year. This will give us one row per Year.

In [33]:
# Group by Year and then get the max value per year
Year = combined.groupby(lambda x: x.year).max()
Year

Out[33]:
CustomerCount Max BHAG
2009 2452.0 2452.0 NaN
2010 2065.0 2065.0 NaN
2011 2711.0 2711.0 1000.0
2012 2061.0 2061.0 2000.0
2013 NaN NaN 3000.0
In [34]:
# Add a column representing the percent change per year
Year['YR_PCT_Change'] = Year['Max'].pct_change(periods=1)
Year

Out[34]:
CustomerCount Max BHAG YR_PCT_Change
2009 2452.0 2452.0 NaN NaN
2010 2065.0 2065.0 NaN -0.157830
2011 2711.0 2711.0 1000.0 0.312833
2012 2061.0 2061.0 2000.0 -0.239764
2013 NaN NaN 3000.0 0.000000

To get next year's end customer count we will assume our current growth rate remains constant. We then will increase this years customer count by that amount and that will be our forecast for next year.

In [35]:
(1 + Year.loc[2012,'YR_PCT_Change']) * Year.loc[2012,'Max']

Out[35]:
1566.8465510881595

Present Data¶

Create individual Graphs per State.

In [36]:
# First Graph
ALL['Max'].plot(figsize=(10, 5));plt.title('ALL Markets')

# Last four Graphs
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(20, 10))
fig.subplots_adjust(hspace=1.0) ## Create space between plots

`