Lesson 3¶
*These tutorials are also available through an email course, please visit http://www.hedaro.com/pandas-tutorial to sign up today.*
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.*
# Import libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy.random as np
import sys
import matplotlib
%matplotlib inline
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.
# 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.
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
df.head()
| 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.
# 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.
pd.read_excel?
Signature: pd.read_excel( io, sheet_name=0, header=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. .. versionadded:: 0.24.0 * 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. .. versionadded:: 0.24.0 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. .. versionadded:: 0.23.0 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 of reading a large file. 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 don`t 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. See Also -------- to_excel : Write DataFrame to an Excel file. to_csv : Write DataFrame to a comma-separated values (csv) file. read_csv : Read a comma-separated values (csv) file into DataFrame. read_fwf : Read a table of fixed-width formatted lines into DataFrame. 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 >>> pd.read_excel(open('tmp.xlsx', 'rb'), ... 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 >>> pd.read_excel('tmp.xlsx', index_col=None, header=None) # doctest: +SKIP 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 >>> pd.read_excel('tmp.xlsx', index_col=0, ... 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! >>> pd.read_excel('tmp.xlsx', index_col=0, ... 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.
# Location of file
Location = r'C:\notebooks\pandas\Lesson3.xlsx'
# Parse a specific sheet
df = pd.read_excel(Location, 0, index_col='StatusDate')
df.dtypes
State object Status int64 CustomerCount int64 dtype: object
df.index
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)
df.head()
| 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.
- Make sure the state column is all in upper case
- Only select records where the account status is equal to "1"
- Merge (NJ and NY) to NY in the state column
- 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
df['State'].unique()
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.
# Clean State Column, convert to upper case
df['State'] = df.State.apply(lambda x: x.upper())
df['State'].unique()
array(['GA', 'FL', 'TX', 'NY', 'NJ'], dtype=object)
# Only grab where Status == 1
mask = df['Status'] == 1
df = df[mask]
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.
# Convert NJ to NY
mask = df.State == 'NJ'
df['State'][mask] = 'NY'
Now we can see we have a much cleaner data set to work with.
df['State'].unique()
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.
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.
sortdf = df[df['State']=='NY'].sort_index(axis=0)
sortdf.head(10)
| 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.
# Group by State and StatusDate
Daily = df.reset_index().groupby(['State','StatusDate']).sum()
Daily.head()
| 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.
del Daily['Status']
Daily.head()
| CustomerCount | ||
|---|---|---|
| State | StatusDate | |
| FL | 2009-01-12 | 901 |
| 2009-02-02 | 653 | |
| 2009-03-23 | 752 | |
| 2009-04-06 | 1086 | |
| 2009-06-08 | 649 |
# What is the index of the dataframe
Daily.index
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)
# Select the State index
Daily.index.levels[0]
Index(['FL', 'GA', 'NY', 'TX'], dtype='object', name='State')
# Select the StatusDate index
Daily.index.levels[1]
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?
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.
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.
# 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.
Daily.head()
| 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.
# 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())
ALL.head()
| 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.
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 See Also -------- 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
# 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
| 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.
# Combine the BHAG and the ALL data set
combined = pd.concat([ALL,BHAG], axis=0)
combined = combined.sort_index(axis=0)
combined.tail()
| 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 |
fig, axes = plt.subplots(figsize=(12, 7))
combined['BHAG'].fillna(method='pad').plot(color='green', label='BHAG')
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.
# Group by Year and then get the max value per year
Year = combined.groupby(lambda x: x.year).max()
Year
| 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 |
# Add a column representing the percent change per year
Year['YR_PCT_Change'] = Year['Max'].pct_change(periods=1)
Year
| 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.
(1 + Year.loc[2012,'YR_PCT_Change']) * Year.loc[2012,'Max']
1566.8465510881595
Present Data¶
Create individual Graphs per State.