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The Truth About Guns

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Are you ready to head back to Middle Earth? Amazon Studios revealed the title and trailer Wednesday for its highly anticipated prequel to the “Lord of the Rings” series, called “The Lord of the Rings: The Rings of Power.”

The series will debut on Prime Video on Sept. 2.

“The Rings of Power” is set in the Second Age of Middle Earth, thousands of years before the events of J.R.R. Tolkien’s “The Hobbit” and “The Lord of the Rings.”

The series “will take viewers back to an era in which great powers were forged, kingdoms rose to glory and fell to ruin, unlikely heroes were tested, hope hung by the finest of threads, and the greatest villain that ever flowed from Tolkien’s pen threatened to cover all the world in darkness,” Amazon said in its YouTube description for the trailer.

Amazon founder Jeff Bezos tweeted an image of himself holding a big slab of wood with the series title on it. “Can’t wait for you to see it,” he wrote.

IGN has behind-the-scenes details on how the title sequence was created, and it wasn’t with CGI, but rather with molten metal and a “hunk of reclaimed redwood.”

Coming this September 2nd. Can’t wait for you to see it. @LOTRonPrime (1/2) pic.twitter.com/jd0wrPIJAp

— Jeff Bezos (@JeffBezos) January 19, 2022

Amazon first announced that it had acquired the rights to adapt Tolkien’s work in 2017.

“’The Lord of the Rings’ is a cultural phenomenon that has captured the imagination of generations of fans through literature and the big screen,” Sharon Tal Yguado, head of Scripted Series for Amazon Studios, said in a statement at the time. 

Tolkien’s book series was named Amazon customers’ favorite book of the millennium in 1999. Director Peter Jackson’s theatrical adaptations included “The Fellowship of the Ring” (2001); “The Two Towers” (2002); and “The Return of the King” (2003). The films grossed nearly $6 billion worldwide and won a combined 17 Academy Awards, including Best Picture for “King.”

GeekWire

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Link

Rhys Darby (Murray from Flight of the Conchords) stars in this high-seas comedy adventure series about a wealthy man who abandons his life of privilege to become a pirate. Taika Waititi, the busiest man in Hollywood, does double duty as Executive Producer and performs as Blackbeard. Premieres 3.2022 on HBO Max.

The Awesomer

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The Pandas DataFrame/Series has several methods to handle Missing Data. When applied to a DataFrame/Series, these methods evaluate and modify the missing elements.

This is Part 13 of the DataFrame methods series:

• Part 1 focuses on the DataFrame methods abs(), all(), any(), clip(), corr(), and corrwith().
• Part 2 focuses on the DataFrame methods count(), cov(), cummax(), cummin(), cumprod(), cumsum().
• Part 3 focuses on the DataFrame methods describe(), diff(), eval(), kurtosis().
• Part 4 focuses on the DataFrame methods mad(), min(), max(), mean(), median(), and mode().
• Part 5 focuses on the DataFrame methods pct_change(), quantile(), rank(), round(), prod(), and product().
• Part 6 focuses on the DataFrame methods add_prefix(), add_suffix(), and align().
• Part 7 focuses on the DataFrame methods at_time(), between_time(), drop(), drop_duplicates() and duplicated().
• Part 8 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 9 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 10 focuses on the DataFrame methods reset_index(), sample(), set_axis(), set_index(), take(), and truncate()
• Part 11 focuses on the DataFrame methods backfill(), bfill(), fillna(), dropna(), and interpolate()
• Part 12 focuses on the DataFrame methods isna(), isnull(), notna(), notnull(), pad() and replace()
• Part 13 focuses on the DataFrame methods drop_level(), pivot(), pivot_table(), reorder_levels(), sort_values() and sort_index()

Getting Started

Remember to add the Required Starter Code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

Required Starter Code

import pandas as pd
import numpy as np 

Before any data manipulation can occur, two new libraries will require installation.

• The pandas library enables access to/from a DataFrame.
• The numpy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install numpy

Hit the <Enter> key on the keyboard to start the installation process.

Feel free to check out the correct ways of installing those libraries here:

• How to Install Pandas on PyCharm?
• How to Install NumPy on PyCharm?

If the installations were successful, a message displays in the terminal indicating the same.

DataFrame drop_level()

The drop_level() method removes the specified index or column from a DataFrame/Series. This method returns a DataFrame/Series with the said level/column removed.

The syntax for this method is as follows:

DataFrame.droplevel(level, axis=0)

Parameter	Description
level	If the level is a string, this level must exist. If a list, the elements must exist and be a level name/position of the index.
axis	If zero (0) or index is selected, apply to each column. Default is 0 (column). If zero (1) or columns, apply to each row.

For this example, we generate random stock prices and then drop (remove) level Stock-B from the DataFrame.

nums = np.random.uniform(low=0.5, high=13.3, size=(3,4))
df_stocks = pd.DataFrame(nums).set_index([0, 1]).rename_axis(['Stock-A', 'Stock-B'])
print(df_stocks)

result = df_stocks.droplevel('Stock-B')
print(result)

• Line [1] generates random numbers for three (3) lists within the specified range. Each list contains four (4) elements (size=3,4). The output saves to nums.
• Line [2] creates a DataFrame, sets the index, and renames the axis. This output saves to df_stocks.
• Line [3] outputs the DataFrame to the terminal.
• Line [4] drops (removes) Stock-B from the DataFrame and saves it to the result variable.
• Line [5] outputs the result to the terminal.

Output:

df_stocks

		2	3
Stock-A	Stock-B
12.327710	10.862572	7.105198	8.295885
11.474872	1.563040	5.915501	6.102915

result

	2	3
Stock-A
12.327710	7.105198	8.295885
11.474872	5.915501	6.102915

DataFrame pivot()

The pivot() method reshapes a DataFrame/Series and produces/returns a pivot table based on column values.

The syntax for this method is as follows:

DataFrame.pivot(index=None, columns=None, values=None)

Parameter	Description
index	This parameter can be a string, object, or a list of strings and is optional. This option makes up the new DataFrame/Series index. If None, the existing index is selected.
columns	This parameter can be a string, object, or a list of strings and is optional. Makes up the new DataFrame/Series column(s).
values	This parameter can be a string, object, or a list of the previous and is optional.

For this example, we generate 3-day sample stock prices for Rivers Clothing. The column headings display the following characters.

• A (for Opening Price)
• B (for Midday Price)
• C (for Opening Price)

cdate_idx = ['01/15/2022', '01/16/2022', '01/17/2022'] * 3
group_lst = list('AAABBBCCC')
vals_lst  = np.random.uniform(low=0.5, high=13.3, size=(9))

df = pd.DataFrame({'dates':  cdate_idx,
                                    'group':  group_lst,
                                   'value':  vals_lst})
print(df)

result = df.pivot(index='dates', columns='group', values='value')
print(result)

• Line [1] creates a list of dates and multiplies this by three (3). The output is three (3) entries for each date. This output saves to cdate_idx.
• Line [2] creates a list of headings for the columns (see above for definitions). Three (3) of each character are required (9 characters). This output saves to group_lst.
• Line [3] uses np.random.uniform to create a random list of nine (9) numbers between the set range. The output saves to vals_lst.
• Line [4] creates a DataFrame using all the variables created on lines [1-3]. The output saves to df.
• Line [5] outputs the DataFrame to the terminal.
• Line [6] creates a pivot from the DataFrame and groups the data by dates. The output saves to result.
• Line [7] outputs the result to the terminal.

Output:

df

	dates	group	value
0	01/15/2022	A	9.627767
1	01/16/2022	A	11.528057
2	01/17/2022	A	13.296501
3	01/15/2022	B	2.933748
4	01/16/2022	B	2.236752
5	01/17/2022	B	7.652414
6	01/15/2022	C	11.813549
7	01/16/2022	C	11.015920
8	01/17/2022	C	0.527554

result

group	A	B	C
dates
01/15/2022	8.051752	9.571285	6.196394
01/16/2022	6.511448	8.158878	12.865944
01/17/2022	8.421245	1.746941	12.896975

DataFrame pivot_table()

The pivot_table() method streamlines a DataFrame to contain only specific data (columns). For example, say we have a list of countries with associated details. We only want to display one or two columns. This method can accomplish this task.

The syntax for this method is as follows:

DataFrame.pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True)

Parameter	Description
values	This parameter is the column to aggregate and is optional.
index	If the parameter is an array, it must be the same length as the data. It may contain any other data types (but not a list).
columns	If an array, it must be the same length as the data. It may contain any other data types (but not a list).
aggfunc	This parameter can be a list of functions. These name(s) will display at the top of the relevant column names (see Example 2).
fill_value	This parameter is the value used to replace missing values in the table after the aggregation has occurred.
margins	If set to True, this parameter will add the row/column data to create subtotal(s) or total(s). False, by default.
dropna	This parameter will not include any columns where the value(s) are NaN. True by default.
margins_name	This parameter is the name of the row/column containing the totals if margins parameter is True.
observed	If True, display observed values. If False, display all observed values.
sort	By default, sort is True. The values automatically sort. If False, no sort is applied.

For this example, a comma-delimited CSV file is read in. A pivot table is created based on selected parameters.

Code – Example 1:

df = pd.read_csv('countries.csv')
df = df.head(5)
print(df)

result = pd.pivot_table(df, values='Population', columns='Capital')
print(result)

• Line [1] reads in a CSV file and saves to a DataFrame (df).
• Line [2] saves the first five (5) rows of the CSV file to df (over-writing df).
• Line [3] outputs the DataFrame to the terminal.
• Line [4] creates a pivot table from the DataFrame based on the Population and Capital columns. The output saves to result.
• Line [5] outputs the result to the terminal.

Output:

df

	Country	Capital	Population	Area
0	Germany	Berlin	83783942	357021
1	France	Paris	67081000	551695
2	Spain	Madrid	47431256	498511
3	Italy	Rome	60317116	301338
4	Poland	Warsaw	38383000	312685

result

Capital	Berlin	Madrid	Paris	Rome	Warsaw
Population	83783942	47431256	67081000	60317116	38383000

For this example, a comma-delimited CSV file is read in. A pivot table is created based on selected parameters. Notice the max function.

Code – Example 2

df = pd.read_csv('countries.csv')
df = df.head(5)

result = pd.pivot_table(df, values='Population', columns='Capital', aggfunc=[max])
print(result)

• Line [1] reads in a comma-separated CSV file and saves to a DataFrame (df).
• Line [2] saves the first five (5) rows of the CSV file to df (over-writing df).
• Line [3] creates a pivot table from the DataFrame based on the Population and Capital columns. The max population is a parameter of aggfunc. The output saves to result.
• Line [4] outputs the result to the terminal.

Output:

result

	max
Capital	Berlin	Madrid	Paris	Rome	Warsaw
Population	83783942	47431256	67081000	60317116	38383000

DataFrame reorder_levels()

The reorder_levels() method re-arranges the index of a DataFrame/Series. This method can not contain any duplicate level(s) or drop level(s).

The syntax for this method is as follows:

DataFrame.reorder_levels(order, axis=0)

Parameter	Description
order	This parameter is a list containing the new order levels. These levels can be a position or a label.
axis	If zero (0) or index is selected, apply to each column. Default is 0 (column). If zero (1) or columns, apply to each row.

For this example, there are five (5) students. Each student has some associated data with it. Grades generate by using np.random.randint().

index = [(1001, 'Micah Smith', 14), (1001, 'Philip Jones', 15), 
         	(1002, 'Ben Grimes', 16), (1002, 'Alicia Heath', 17), (1002, 'Arch Nelson', 18)]
m_index = pd.MultiIndex.from_tuples(index)
grades_lst = np.random.randint(45,100,size=5)
df = pd.DataFrame({"Grades": grades_lst}, index=m_index)
print(df)

result = df.reorder_levels([1,2,0])
print(result)

• Line [1] creates a List of tuples. Each tuple contains three (3) values. The output saves to index.
• Line [2] creates a MultiIndex from the List of Tuples created on line [1] and saves to m_index.
• Line [3] generates five (5) random grades between the specified range and saves to grades_lst.
• Line [4] creates a DataFrame from the variables on lines [1-3] and saves to df.
• Line [5] outputs the DataFrame to the terminal.
• Line [6] re-orders the levels as specified. The output saves to result.
• Line [7] outputs the result to the terminal.

Output:

df

			Grades
1001	Micah Smith	14	52
	Philip Jones	15	65
1002	Ben Grimes	16	83
	Alicia Heath	17	99
	Arch Nelson	18	78

result

			Grades
Micah Smith	14	1001	52
Philip Jones	15	1001	65
Ben Grimes	16	1002	83
Alicia Heath	17	1002	99
Arch Nelson	18	1002	78

DataFrame sort_values()

The sort_values() method sorts (re-arranges) the elements of a DataFrame.

The syntax for this method is as follows:

DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)

Parameter	Description
by	This parameter is a string or a list of strings. These comprise the index levels/columns to sort. Dependent on the selected axis.
axis	If zero (0) or index is selected, apply to each column. Default is 0 (column). If zero (1) or columns, apply to each row.
ascending	By default, True. Sort is conducted in ascending order. If False, descending order.
inplace	If False, create a copy of the object. If True, the original object updates. By default, False.
kind	Available options are quicksort, mergesort, heapsort, or stable. By default, quicksort. See numpy.sort for additional details.
na_position	Available options are first and last (default). If the option is first, all NaN values move to the beginning, last to the end.
ignore_index	If True, the axis numbering is 0, 1, 2, etc. By default, False.
key	This parameter applies the function to the values before a sort. The data must be in a Series format and applies to each column.

For this example, a comma-delimited CSV file is read in. This DataFrame sorts on the Capital column in descending order.

df = pd.read_csv('countries.csv')
result = df.sort_values(by=['Capital'], ascending=False)
print(result)

• Line [1] reads in a comma-delimited CSV file and saves to df.
• Line [2] sorts the DataFrame on the Capital column in descending order. The output saves to result.
• Line [3] outputs the result to the terminal.

Output:

	Country	Capital	Population	Area
6	USA	Washington	328239523	9833520
4	Poland	Warsaw	38383000	312685
3	Italy	Rome	60317116	301338
1	France	Paris	67081000	551695
5	Russia	Moscow	146748590	17098246
2	Spain	Madrid	47431256	498511
8	India	Dheli	1352642280	3287263
0	Germany	Berlin	83783942	357021
7	India	Beijing	1400050000	9596961

DataFrame sort_index()

The sort_index() method sorts the DataFrame.

The syntax for this method is as follows:

DataFrame.sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)

Parameter	Description
axis	If zero (0) or index is selected, apply to each column. Default is 0 (column). If zero (1) or columns, apply to each row.
level	This parameter is an integer, level name, or a list of integers/level name(s). If not empty, a sort is performed on values in the selected index level(s).
ascending	By default, True. Sort is conducted in ascending order. If False, descending order.
inplace	If False, create a copy of the object. If True, the original object updates. By default, False.
kind	Available options are quicksort, mergesort, heapsort, or stable. By default, quicksort. See numpy.sort for additional details.
na_position	Available options are first and last (default). If the option is first, all NaN values move to the beginning, last to the end.
ignore_index	If True, the axis numbering is 0, 1, 2, etc. By default, False.
key	This parameter applies the function to the values before a sort. The data must be in a Series format and applies to each column.

For this example, a comma-delimited CSV file is read into a DataFrame. This DataFrame sorts on the index Country column.

df = pd.read_csv('countries.csv')
df = df.set_index('Country')
result = df.sort_index()
print(result)

• Line [1] reads in a comma-delimited CSV file and saves to df.
• Line [2] sets the index of the DataFrame to Country. The output saves to df (over-writing original df).
• Line [3] sorts the DataFrame (df) on the indexed column (Country) in ascending order (default). The output saves to result.
• Line [4] outputs the result to the terminal.

Output:

	Country	Population	Area
China	Beijing	1400050000	9596961
France	Paris	67081000	551695
Germany	Berlin	83783942	357021
India	Dheli	1352642280	3287263
Italy	Rome	60317116	301338
Poland	Warsaw	38383000	312685
Russia	Moscow	146748590	17098246
Spain	Madrid	47431256	498511
USA	Washington	328239523	9833520

Finxter

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Boosting levels of the neurotransmitter norepinephrine with atomoxetine, a repurposed ADHD medication, may be able to stall neurodegeneration in people with early signs of Alzheimer’s disease, according to a new study.

The results appear in the journal Brain.

This is one of the first published clinical studies to show a significant effect on the protein tau, which forms neurofibrillary tangles in the brain in Alzheimer’s. In 39 people with mild cognitive impairment (MCI), six months of treatment with atomoxetine reduced levels of tau in study participants’ cerebrospinal fluid (CSF), and normalized other markers of neuro-inflammation.

The study points toward an alternative drug strategy against Alzheimer’s that does not rely on antibodies against tau or another Alzheimer’s-related protein, beta-amyloid. A recent FDA-approved drug, adacanumab, targets beta-amyloid but its benefits are controversial among experts in the field.

Larger and longer studies of atomoxetine in MCI and Alzheimer’s are warranted, the researchers conclude. The drug did not have a significant effect on cognition or other clinical outcomes, which was expected given the relatively short study duration.

“One of the major advantages of atomoxetine is that it is already FDA-approved and known to be safe,” says senior author David Weinshenker, professor of human genetics at Emory University School of Medicine. “The beneficial effects of atomoxetine on both brain network activity and CSF markers of inflammation warrant optimism.”

“We are encouraged by the results of the trial,” says lead author Allan Levey, professor of neurology at Emory University School of Medicine and director of the Goizueta Institute @Emory Brain Health. “The treatment was safe, well tolerated in individuals with mild cognitive impairment, and modulated the brain neurotransmitter norepinephrine just as we hypothesized. Moreover, our exploratory studies show promising results on imaging and spinal fluid biomarkers which need to be followed up in larger studies with longer period of treatment.”

The researchers picked atomoxetine, which is commercially available as Strattera, with the goal of boosting brain levels of norepinephrine, which they thought could stabilize a vulnerable region of the brain against Alzheimer’s-related neurodegeneration.

Norepinephrine is produced mainly by the locus coeruleus, a region of the brainstem that appears to be the first to show Alzheimer’s-related pathology—even in healthy, middle-aged people. Norepinephrine is thought to reduce inflammation and to encourage trash-removing cells called microglia to clear out aggregates of proteins such as beta-amyloid and tau. Increasing norepinephrine levels has positive effects on cognition and pathology in mouse and rat models of Alzheimer’s.

“Something that might seem obvious, but was absolutely essential, was our finding that atomoxetine profoundly increased CSF norepinephrine levels in these patients,” Weinshenker says. “For many drugs and trials, it is very difficult to prove target engagement. We were able to directly assess target engagement.”

Weinshenker also emphasizes that the trial grew out of pre-clinical research conducted in animal models, which demonstrated the potential for norepinephrine.

The researchers conducted the study between 2012 and 2018 with a cross-over design, such that half the group received atomoxetine for the first six months and the other half received placebo—then individuals switched. It is possible that participants who received atomoxetine for the first six months experienced carryover effects after treatment stopped, so their second six month period wasn’t necessarily a pure placebo.

Study participants were all diagnosed with mild cognitive impairment and had markers of potential progression to Alzheimer’s in their CSF, based on measuring tau and beta-amyloid. More information about inclusion criteria is available at clinicaltrials.gov.

The researchers measured levels of dozens of proteins in participants’ CSF; the reduction of tau from atomoxetine treatment was small—about 5% over six months—but if sustained, it could have a larger effect on Alzheimer’s pathology. No significant effect on beta-amyloid was seen.

In addition, in participants taking atomoxetine, researchers were able to detect an increase in metabolism in the medial temporal lobe, critical for memory, via PET (positron emission tomography) brain imaging.

Study participants started with a low dose of atomoxetine and ramped up to a higher dose, up to 100mg per day. Participants did experience weight loss (4 pounds, on average) and an increase in heart rate (about 5 beats per minute) while on atomoxetine, but they did not display a significant increase in blood pressure. Some people reported side effects such as gastrointestinal symptoms, dry mouth, or dizziness.

The FDA approved atomoxetine in 2002 for ADHD (attention deficit hyperactivity disorder) in children and adults, and the drug has been shown to be safe in older adults. It is considered to have low abuse potential, compared with conventional stimulants that are commonly prescribed for ADHD.

Looking ahead, it is now possible to visualize the integrity of the locus coeruleus in living people using MRI techniques, so that could be an important part of a larger follow-up study, Weinshenker says. Atomoxetine’s effects were recently studied in people with Parkinson’s disease—the benefits appear to be greater in those who have reduced integrity of the locus coeruleus.

Funding for the study was provided by the Cox and Kenan Family foundations and the Alzheimer’s Drug Discovery Foundation.

Source: Emory University

The post ADHD drug may protect against Alzheimer’s neurodegeneration appeared first on Futurity.

Futurity

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The Pandas DataFrame/Series has several methods to handle Missing Data. When applied to a DataFrame/Series, these methods evaluate and modify the missing elements.

This is Part 12 of the DataFrame methods series:

• Part 1 focuses on the DataFrame methods abs(), all(), any(), clip(), corr(), and corrwith().
• Part 2 focuses on the DataFrame methods count(), cov(), cummax(), cummin(), cumprod(), cumsum().
• Part 3 focuses on the DataFrame methods describe(), diff(), eval(), kurtosis().
• Part 4 focuses on the DataFrame methods mad(), min(), max(), mean(), median(), and mode().
• Part 5 focuses on the DataFrame methods pct_change(), quantile(), rank(), round(), prod(), and product().
• Part 6 focuses on the DataFrame methods add_prefix(), add_suffix(), and align().
• Part 7 focuses on the DataFrame methods at_time(), between_time(), drop(), drop_duplicates() and duplicated().
• Part 8 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 9 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 10 focuses on the DataFrame methods reset_index(), sample(), set_axis(), set_index(), take(), and truncate()
• Part 11 focuses on the DataFrame methods backfill(), bfill(), fillna(), dropna(), and interpolate()
• Part 12 focuses on the DataFrame methods isna(), isnull(), notna(), notnull(), pad() and replace()

Getting Started

Remember to add the Required Starter Code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

Required Starter Code

import pandas as pd
import numpy as np 

Before any data manipulation can occur, two new libraries will require installation.

• The pandas library enables access to/from a DataFrame.
• The numpy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install numpy

Hit the <Enter> key on the keyboard to start the installation process.

Feel free to check out the correct ways of installing those libraries here:

• How to Install Pandas on PyCharm?
• How to Install NumPy on PyCharm?

If the installations were successful, a message displays in the terminal indicating the same.

DataFrame isna() & Dataframe isnull()

The DataFrame isna() and isnull() methods return Boolean (True/False) values in the same shape as the DataFrame/Series passed. If any empty values are of the following type, they will resolve to True.

• None
• NaN
• NaT
• NA

All other values (valid data) will resolve to False.

Note: Any empty strings or numpy.inf are not considered empty unless use_inf_as_na is set to True.

The syntax for these methods is as follows:

DataFrame.isna()
DataFrame.isnull()

Parameters:

These methods contain no parameters.

For this example, three (3) temperatures over three (3) days for Anchorage, Alaska save to a DataFrame. Unfortunately, some temperatures did not accurately record.

The code below returns a new DataFrame containing True values in the same position as the missing temperatures and False in the remainder.

Code – isna():

df_temps = pd.DataFrame({'Day-1':  [np.nan, 11, 12], 
                         'Day-2':  [13, 14, pd.NaT],
                         'Day-3':  [None, 15, 16]},
                         index=['Morning', 'Noon', 'Evening'])
print(df_temps)

result = df_temps.isna()
print(result)

• Line [1] creates a dictionary of lists and saves it to df_temps.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] uses isna() to set the empty values (np.nan, pd.NaT, None) to True and the remainder (valid values) to False. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output:

original df_temps

	Day-1	Day-2	Day-3
Morning	NaN	13	NaN
Noon	11.0	14	15.0
Evening	12.0	NaT	16.0

result

	Day-1	Day-2	Day-3
Morning	True	False	True
Noon	False	False	False
Evening	False	True	False

Code – isnull():

df_temps = pd.DataFrame({'Day-1':  [np.nan, 11, 12], 
                   'Day-2':  [13, 14, pd.NaT],
                   'Day-3':  [None, 15, 16]},
                   index=['Morning', 'Noon', 'Evening'])
print(df_temps)

result = df_temps.isnull()
print(result)

• Line [1] creates a dictionary of lists and saves it to df_temps.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] uses isnull() to set the empty values (np.nan, pd.NaT, None) to True and the remainder (valid values) to False. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output:

original df_temps

	Day-1	Day-2	Day-3
Morning	NaN	13	NaN
Noon	11.0	14	15.0
Evening	12.0	NaT	16.0

result

	Day-1	Day-2	Day-3
Morning	True	False	True
Noon	False	False	False
Evening	False	True	False

Note: The isnull() method is an alias of the isna() method. The output from both examples is identical.

DataFrame notna() & notnull()

The DataFrame notna() and notnull() methods return Boolean (True/False) values. These values returned are in the same shape as the DataFrame/Series passed. If any empty values are of the following type, they will resolve to False.

• None
• NaN
• NaT
• NA

All other values that are not of the above type (valid data) will resolve to True.

The syntax for these methods is as follows:

DataFrame.notna()
DataFrame.notnull()

Parameters:

These methods contain no parameters.

For this example, three (3) temperatures over three (3) days for Anchorage, Alaska save to a DataFrame. Unfortunately, some temperatures did not accurately record.

The code below returns a new DataFrame containing True values in the same position as the missing temperatures and False in the remainder.

Code – notna():

df_temps = pd.DataFrame({'Day-1':  [np.nan, 11, 12], 
                   'Day-2':  [13, 14, pd.NaT],
                   'Day-3':  [None, 15, 16]},
                   index=['Morning', 'Noon', 'Evening'])
print(df_temps)

result = df_temps.notna()
print(result)

• Line [1] creates a dictionary of lists and saves it to df_temps.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] uses notna() to set the empty values (np.nan, pd.NaT, None) to False and the remainder (valid values) to True. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output:

original df_temps

	Day-1	Day-2	Day-3
Morning	NaN	13	NaN
Noon	11.0	14	15.0
Evening	12.0	NaT	16.0

result

	Day-1	Day-2	Day-3
Morning	False	True	False
Noon	True	True	True
Evening	True	False	True

Code – notnull():

df_temps = pd.DataFrame({'Day-1':  [np.nan, 11, 12], 
                   'Day-2':  [13, 14, pd.NaT],
                   'Day-3':  [None, 15, 16]},
                   index=['Morning', 'Noon', 'Evening'])
print(df_temps)

result = df_temps.notnull()
print(result)

• Line [1] creates a dictionary of lists and saves it to df_temps.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] uses notnull() to set the empty values (np.nan, pd.NaT, None) to False and the remainder (valid values) to True. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output:

original df_temps

	Day-1	Day-2	Day-3
Morning	NaN	13	NaN
Noon	11.0	14	15.0
Evening	12.0	NaT	16.0

result

	Day-1	Day-2	Day-3
Morning	False	True	False
Noon	True	True	True
Evening	True	False	True

Note: The notnull() method is an alias of the notna() method. The output from both examples is identical.

DataFrame pad()

The pad() method is an alias for DataFrame/Series fillna() with the parameter method set to 'ffill'. Click here for details.

DataFrame replace()

The replace() method substitutes values in a DataFrame/Series with a different value assigned. This operation is performed dynamically on the object passed.

Note: The .loc/.iloc methods are slightly different from replace() as they require a specific location in order to change the said value(s).

The syntax for this method is as follows:

DataFrame.replace(to_replace=None, value=None, 
                  inplace=False, limit=None, 
                  regex=False, method='pad')

Parameter	Description
to_replace	Determines how to locate values to replace. The following parameters are: – Numeric, String, or Regex. – List of Strings, Regex, or Numeric. – Dictionary: a Dictionary, DataFrame Dictionary, or Nested Dictionary Each one must exactly match the to_replace parameter to cause any change.
value	The value to replace any values that match.
inplace	If set to True, the changes apply to the original DataFrame/Series. If False, the changes apply to a new DataFrame/Series. By default, False.
limit	The maximum number of elements to backward/forward fill.
regex	A regex expression to match. Matches resolve to the value parameter.
method	The available options for this method are pad, ffill, bfill, or None. Specify the replacement method to use.

Possible Errors Raised:

Error	When Does It Occur?
AssertionError	If regex is not a Boolean (True/False), or the to_replace parameter is None.
TypeError	If to_replace is not in a valid format, such as: – Not scalar, an array, a dictionary, or is None. – If to_replace is a dictionary and the value parameter is not a list. – If multiple Booleans or date objects and to_replace fails to match the value parameter.
ValueError	Any error returns if a list/ndarray and value are not the same length.

The examples below show how versatile the replace() method is. We recommend you spend some time reviewing the code and output.

In this example, we have five (5) grades for a student. Notice that one (1) grade is a failing grade. To rectify this, run the following code:

Code – Example 1

grades = pd.Series([55, 64, 52, 76, 49])
print(grades)

result = grades.replace(49, 51)
print(result)

• Line [1] creates a Series of Lists and saves it to grades.
• Line [2] modifies the failing grade of 49 to a passing grade of 51. The output saves to result.
• Line [3] outputs the result to the terminal.

Output:

O	55
1	64
2	52
3	76
4	51
dtype: int64

This example shows a DataFrame of three (3) product lines for Rivers Clothing. They want the price of 11.35 changed to 12.95. Run the code below to change the pricing.

Code – Example 2:

df = pd.DataFrame({'Tops':     [10.12, 12.23, 11.35],
                   'Tanks':    [11.35, 13.45, 14.98],
                   'Sweats':  [11.35, 21.85, 35.75]})

result = df.replace(11.35, 12.95)
print(result)

• Line [1] creates a dictionary of lists and saves it to df.
• Line [2] replaces the value 11.35 to 12.95 for each occurrence. The output saves to result.
• Line [3] outputs the result to the terminal.

Output:

	Tops	Tanks	Sweats
0	10.12	12.95	12.95
1	12.23	13.45	21.85
2	12.95	14.98	35.75

Code – Example 3:

This example shows a DataFrame with two (2) teams. Each team contains three (3) members. This code removes one (1) member from each team and replaces it with quit.

df = pd.DataFrame({'Team-1': ['Barb', 'Todd', 'Taylor'],
                   'Team-2': ['Arch', 'Bart', 'Alex']})

result = df.replace(to_replace=r'^Bar.$', value='quit', regex=True)
print(result)

• Line [1] creates a Dictionary of Lists and saves it to df.
• Line [2] replaces any values that start with Bar and contain one (1) additional character (.). This match changed to the word quit. The output saves to result.
• Line [3] outputs the result to the terminal.

Finxter

https://static1.makeuseofimages.com/wordpress/wp-content/uploads/2022/01/shaking_hands_interview.jpg

MUO – Feed

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Gizmodo

I believe that armed self-defense is an extension of the Christian’s mandate to protect the innocent, to “watch over widows and orphans in their distress.” No one doubts that a policeman who carries a gun and watches over society can do such things and still be a Christian: Why, therefore, is there any doubt that an armed individual like me can carry a gun and watch over a small portion of society (my family) and yet still have a deep, abiding faith in God?

The Biblical Basis for Self-Defense – Ricochet

Go read the whole thing.

I like to say I am the most peaceful man you will ever know, but I am not a Pacifist.  Not understanding that difference can be hurtful to your health and ability to remain above room temperature. This does not counter the doctrine of the Catholic Church or any real Christian belief. It does piss off something fierce the Lefties using alleged Christian values and political jump points for the most antichrist behavior they can think of.

https://assets.amuniversal.com/c7a90d003b9f013a8b1f005056a9545d

Dilbert Daily Strip