determining the bounds of a tuple returned from a database

[ronrsr]

here is my result.
How do I determine the number of tuples in this array, returned from a
mysql database?

How do I determine the number of characters or entry in each tuple?

thanks very much for your assistance,

-rsr-
(('Agricultural subsidies; Foreign aid',), ('Agriculture; Sustainable
Agriculture - Support; Organic Agriculture; Pesticides, US, Childhood
Development, Birth Defects; Toxic Chemicals',), ('Antibiotics,
Animals',), ('Agricultural Subsidies, Global Trade',), ('Agricultural
Subsidies',), ('Biodiversity',), ('Citizen Activism',), ('Community
Gardens',), ('Cooperatives',), ('Dieting',), ('Agriculture, Cotton',),
('Agriculture, Global Trade',), ('Pesticides, Monsanto',),
('Agriculture, Seed',), ('Coffee, Hunger',), ('Pollution, Water,
Feedlots',), ('Food Prices',), ('Agriculture, Workers',), ('Animal
Feed, Corn, Pesticides',), ('Aquaculture',), ('Chemical Warfare',),
('Compost',), ('Debt',), ('Consumerism',), ('Fear',), ('Pesticides, US,
Childhood Development, Birth Defects',), ('Corporate Reform,
Personhood (Dem. Book)',), ('Corporate Reform, Personhood, Farming
(Dem. Book)',), ('Crime Rates, Legislation, Education',), ('Debt,
Credit Cards',), ('Democracy',), ('Population, World',), ('Income',),
('Democracy, Corporate Personhood, Porter Township (Dem. Book)',),
('Disaster Relief',), ('Dwellings, Slums',), ('Economics, Mexico',),
('Economy, Local',), ('Education, Protests',), ('Endangered Habitat,
Rainforest',), ('Endangered Species',), ('Endangered Species,
Extinction',), ('antibiotics, livestock',), ('Pesticides, Water',),
('Environment, Environmentalist',), ('Food, Hunger, Agriculture, Aid,
World, Development',), ('Agriculture, Cotton Trade',), ('Agriculture,
Cotton, Africa',), ('Environment, Energy',), ('Fair Trade (Dem.
Book)',), ('Farmland, Sprawl',), ('Fast Food, Globalization,
Mapping',), ('depression, mental illness, mood disorders',), ('Economic
Democracy, Corporate Personhood',), ('Brazil, citizen activism, hope,
inspiration, labor issues',), ('citizen activism, advice, hope',),
('Pharmaceuticals, Medicine, Drugs',), ('Community Investing',),
('Environment, Consumer Waste Reduction, Consumer Behavior and
Taxes',), ('Hunger, US, Poverty',), ('FERTILITY, Women',),
('Corporatism, Environment',), ('Economic Democracy, Corporate
Farming',), ('Economic Democracy, Inspiration',), ('FEAR
(Fearlessness)',), ('Federal budget, military spending, foreign
cultural exchange programs',), ('Fish Farming',), ('Fish population,
commercial fishing industry',), ('GMO, BT Cotton',), ('Food borne
Illness, Water',), ('Food Charity',), ('Food Charity, Urban Farming',),
('Food Consumption, Luxury',), ('Food Cost, international',), ('Food
Disposal, Waste',), ('Food stamps, eligibility for',), ('Food,
expenditure on',), ('Foreign Aid',), ('Genetics, Food, Agriculture,
International',), ('Global Policy, Bush Administration',), ('Global
Trade, food',), ('Global Trade, free trade, clothing',),
('Globalization',), ('GMO, SACTO Protests',), ('GMO, Wheat, trade',),
('Happiness and income',), ('Health Care - Spending',), ('Hope',),
('Hope, inspirational quote',), ('Human Cloning',), ('Hunger',),
('Hunger, income',), ('IMF, Argentina',), ('IMF, failings',),
('Inequality',), ('INEQUALITY, GLOBALIZATION, POVERTY',), ('Inequality,
Globalization, Latin America',), ('Inspiration',), ('Junk Food',),
('Junk Food, Soft Drinks',), ('Junk Food ,Children, Education,
Media',), ('Junk Food, Education, Children, Nutrition',), ('Junk Food,
Global, Coke',), ('Junk Food, McDonald\x92s',), ('Junk Food,
McDonald\x92s, Global',), ('Junk Food, Soft Drinks, Water',), ('Junk
Food, Education, Nutrition, Childhood Illness',), ('Junk food,
Nutrition, Schools',), ('Labor',), ('Labor Issues, Agriculture,
Sustainable Agriculture Support',), ('Labor Issues, Employee
Ownership',), ('Land Reform, Brazil, Infant mortality',),
('Livestock',), ('Meat',), ('Meat, Brazil, Soybeans, Deforestation',),
('Meat, concentration',), ('Meat, Recalls, Contamination',), ('Meat,
Recalls',), ('Media',), ('Microloans',), ('Military Spending',),
('Nutrition Programs, School Meals, Snapple',), ('Nutrition,
Finland',), ('Obesity',), ('Obesity, Childhood',), ('Obesity,
Africa',), ('Organic Food',), ('Obesity, Global',), ('Obesity, related
illnesses',), ('Obesity, related illnesses, children',), ('Oil',),
('Organic Agriculture',), ('Organic Agriculture, Growth',), ('Organic
Agriculture, Nutrition',), ('Organic Agriculture, Productivity',),
('Organic food prices',), ('Community',), ('Corporate Concentration',),
('Community Gardens, Urban Gardens',), ('Pollution, Air, CO2
emissions',), ('Population, Water',), ('Poverty, Aid',), ('Poverty,
World Bank, Hunger',), ('Organic Produce',), ('Organic Produce,
Farmer\x92s Markets, CSA Community Initiatives (Dem. Book)',),
('Pakistan, Education, Poverty, Terrorism',), ('Pesticides',),
('Pollution, US',), ('Population, Women',), ('Poverty, Labor Issues',),
('t2',), ('Agriculture, Subsidies, Farming, Ranching, Government,
Livestock, Cattle',), ('School, Farmers\x92 Fresh Produce Food',),
('School, Fast Food, Lunch Programs',), ('School, Fast Food, Lunch
Programs, Harms',), ('Citizens Groups',), ('Public Health, Bacteria',),
('Violence, Impotence',), ('Private Food Aid, Food Stamps',), ('Prison
Rates, Race',), ('Produce, Nutritional content of produce, Historical
Perspective',), ('Public Opinion',), ('Slavery',), ('Solar, Renewable
Energy',), ('Suicide',), ('Sustainable agriculture, Moringa tree',),
('Sweatshop Labor',), ('Tax Cuts, George W. Bush',), ('Economic
Democracy (Dem. Book)',), ('Tobacco',), ('Toxic Chemicals',),
('Trade',), ('Trade, Agricultural Raw materials',), ('Trade, Coffee',),
('Trade, Cotton',), ('Trade, NAFTA, Mexico',), ('Vegetarianism',),
('Veterans\x92 Benefits',), ('Violence',), ('Violence, Media,
Children',), ('Water',), ('Water, Privatization',), ('Welfare
Reform',), ('Wind, Renewable Energy',), ('Words',), ('World
Population',), ('World Bank',), ('Grassroots, Community, Living Wage,
Poverty',), ('Subsidies',), ('Protest, Action',), ('Nuclear Power
Plants, Emissions',), ('Media, Fear, Religion, George W. Bush',),
('Consciousness, Change',), ('Peace, Security, Oil',), ('Globalization,
Inequality, Commodity prices',), ('Water, Corporate',), ('Consumerism,
Developing Nations',), ('Nuclear Power, Wind Power',),
('Sustainability, Hope',), ('Ozone, CFCs, Chlorine, Atmosphere',),
('Hydrogen, Emissions, Ozone',), ('Water, Pesticides',), ('Iraq,
Military, Health',), ('Toxic Waste Reduction, Toxic Waste Disposal,
Environmental Regulation, Recycling Electronics',), ('Shareholder
Advocacy, Social Responsibility, Community Investing (Dem. Book)',),
('Fair Trade',), ('Genetically Modified Food, Africa, USAID, ISAAA',),
('Sustainability, Progress',), ('Sustainability, Human Rights,
Globalization, Corporatization',), ('Agricultural Subsidies,
Agricultural Policy',), ('Agricultural Subsidies, Farmers',), ('Cotton,
Rice',), ('Agricultural Subsidies, Farmers, Livestock',),
('Agricultural Subsidies, Prices',), ('Food scarcity, Hunger, Urban
Agriculture',), ('GMO farming, Toxic chemicals',), ('Hunger,
Children',), ('Hunger, Food Insecurity',), ('Inspiration, Corporatism,
War',), ('Inspiration, Economic Growth',), ('Land Vacancies, US Land
Reform',), ('Local agriculture, Local produce, Nutritional content of
local produce',), ('Local produce, Urban agriculture',), ('Pakistan,
Religion, Education',), ('Pharmaceuticals',), ('Privatization',),
('Public panic, Altruism',), ('QA rewrite: citizen activism,
corporatism-environment, hope, inspiration',), ('Wal-Mart, Food
Stamps',), ('Socially Responsible Investment, Banking and Environmental
Initiatives',), ('Terrorism, Suicide Attacks',), ('Agriculture,
Corporate v. Family Farming',), ('Depression, Health',), ('Depression,
Health, Employment, Work, Employee',), ('Self-Interest, Vote,
Altruism',), ('Animal Feed',), ('Corporate Reform (Dem. Book)',),
('Economic Growth',), ('Corporate History, Globalization',),
('Agriculture',), ('Environment, Public Opinion (Dem. Book)',),
('Illiteracy, poverty',), ('Military, Arms Sales',), ('Agriculture;
Agricultural imports; Global trade',), ('Africa; Foreign aid; Third
World debt; Loan forgiveness; Jubilee',), ('Foreign aid; Military aid;
Development aid; Humanitarian aid',), ('Foreign aid; Development
assistance',), ('Agricultural Subsidies; Foreign aid; Cotton subsidies;
Africa',))

>>>

[ronrsr]

it looks like the len() function is the one I want.

for: len(result) - i get 248,

but for len(result[0]) or len(result[1]) i always get 0.
ronrsr wrote:

here is my result.
How do I determine the number of tuples in this array, returned from a
mysql database?

How do I determine the number of characters or entry in each tuple?

thanks very much for your assistance,

-rsr-
(('Agricultural subsidies; Foreign aid',), ('Agriculture; Sustainable
Agriculture - Support; Organic Agriculture; Pesticides, US, Childhood
Development, Birth Defects; Toxic Chemicals',), ('Antibiotics,
Animals',), ('Agricultural Subsidies, Global Trade',), ('Agricultural
Subsidies',), ('Biodiversity',), ('Citizen Activism',), ('Community
Gardens',), ('Cooperatives',), ('Dieting',), ('Agriculture, Cotton',),
('Agriculture, Global Trade',), ('Pesticides, Monsanto',),
('Agriculture, Seed',), ('Coffee, Hunger',), ('Pollution, Water,
Feedlots',), ('Food Prices',), ('Agriculture, Workers',), ('Animal
Feed, Corn, Pesticides',), ('Aquaculture',), ('Chemical Warfare',),
('Compost',), ('Debt',), ('Consumerism',), ('Fear',), ('Pesticides, US,
Childhood Development, Birth Defects',), ('Corporate Reform,
Personhood (Dem. Book)',), ('Corporate Reform, Personhood, Farming
(Dem. Book)',), ('Crime Rates, Legislation, Education',), ('Debt,
Credit Cards',), ('Democracy',), ('Population, World',), ('Income',),
('Democracy, Corporate Personhood, Porter Township (Dem. Book)',),
('Disaster Relief',), ('Dwellings, Slums',), ('Economics, Mexico',),
('Economy, Local',), ('Education, Protests',), ('Endangered Habitat,
Rainforest',), ('Endangered Species',), ('Endangered Species,
Extinction',), ('antibiotics, livestock',), ('Pesticides, Water',),
('Environment, Environmentalist',), ('Food, Hunger, Agriculture, Aid,
World, Development',), ('Agriculture, Cotton Trade',), ('Agriculture,
Cotton, Africa',), ('Environment, Energy',), ('Fair Trade (Dem.
Book)',), ('Farmland, Sprawl',), ('Fast Food, Globalization,
Mapping',), ('depression, mental illness, mood disorders',), ('Economic
Democracy, Corporate Personhood',), ('Brazil, citizen activism, hope,
inspiration, labor issues',), ('citizen activism, advice, hope',),
('Pharmaceuticals, Medicine, Drugs',), ('Community Investing',),
('Environment, Consumer Waste Reduction, Consumer Behavior and
Taxes',), ('Hunger, US, Poverty',), ('FERTILITY, Women',),
('Corporatism, Environment',), ('Economic Democracy, Corporate
Farming',), ('Economic Democracy, Inspiration',), ('FEAR
(Fearlessness)',), ('Federal budget, military spending, foreign
cultural exchange programs',), ('Fish Farming',), ('Fish population,
commercial fishing industry',), ('GMO, BT Cotton',), ('Food borne
Illness, Water',), ('Food Charity',), ('Food Charity, Urban Farming',),
('Food Consumption, Luxury',), ('Food Cost, international',), ('Food
Disposal, Waste',), ('Food stamps, eligibility for',), ('Food,
expenditure on',), ('Foreign Aid',), ('Genetics, Food, Agriculture,
International',), ('Global Policy, Bush Administration',), ('Global
Trade, food',), ('Global Trade, free trade, clothing',),
('Globalization',), ('GMO, SACTO Protests',), ('GMO, Wheat, trade',),
('Happiness and income',), ('Health Care - Spending',), ('Hope',),
('Hope, inspirational quote',), ('Human Cloning',), ('Hunger',),
('Hunger, income',), ('IMF, Argentina',), ('IMF, failings',),
('Inequality',), ('INEQUALITY, GLOBALIZATION, POVERTY',), ('Inequality,
Globalization, Latin America',), ('Inspiration',), ('Junk Food',),
('Junk Food, Soft Drinks',), ('Junk Food ,Children, Education,
Media',), ('Junk Food, Education, Children, Nutrition',), ('Junk Food,
Global, Coke',), ('Junk Food, McDonald\x92s',), ('Junk Food,
McDonald\x92s, Global',), ('Junk Food, Soft Drinks, Water',), ('Junk
Food, Education, Nutrition, Childhood Illness',), ('Junk food,
Nutrition, Schools',), ('Labor',), ('Labor Issues, Agriculture,
Sustainable Agriculture Support',), ('Labor Issues, Employee
Ownership',), ('Land Reform, Brazil, Infant mortality',),
('Livestock',), ('Meat',), ('Meat, Brazil, Soybeans, Deforestation',),
('Meat, concentration',), ('Meat, Recalls, Contamination',), ('Meat,
Recalls',), ('Media',), ('Microloans',), ('Military Spending',),
('Nutrition Programs, School Meals, Snapple',), ('Nutrition,
Finland',), ('Obesity',), ('Obesity, Childhood',), ('Obesity,
Africa',), ('Organic Food',), ('Obesity, Global',), ('Obesity, related
illnesses',), ('Obesity, related illnesses, children',), ('Oil',),
('Organic Agriculture',), ('Organic Agriculture, Growth',), ('Organic
Agriculture, Nutrition',), ('Organic Agriculture, Productivity',),
('Organic food prices',), ('Community',), ('Corporate Concentration',),
('Community Gardens, Urban Gardens',), ('Pollution, Air, CO2
emissions',), ('Population, Water',), ('Poverty, Aid',), ('Poverty,
World Bank, Hunger',), ('Organic Produce',), ('Organic Produce,
Farmer\x92s Markets, CSA Community Initiatives (Dem. Book)',),
('Pakistan, Education, Poverty, Terrorism',), ('Pesticides',),
('Pollution, US',), ('Population, Women',), ('Poverty, Labor Issues',),
('t2',), ('Agriculture, Subsidies, Farming, Ranching, Government,
Livestock, Cattle',), ('School, Farmers\x92 Fresh Produce Food',),
('School, Fast Food, Lunch Programs',), ('School, Fast Food, Lunch
Programs, Harms',), ('Citizens Groups',), ('Public Health, Bacteria',),
('Violence, Impotence',), ('Private Food Aid, Food Stamps',), ('Prison
Rates, Race',), ('Produce, Nutritional content of produce, Historical
Perspective',), ('Public Opinion',), ('Slavery',), ('Solar, Renewable
Energy',), ('Suicide',), ('Sustainable agriculture, Moringa tree',),
('Sweatshop Labor',), ('Tax Cuts, George W. Bush',), ('Economic
Democracy (Dem. Book)',), ('Tobacco',), ('Toxic Chemicals',),
('Trade',), ('Trade, Agricultural Raw materials',), ('Trade, Coffee',),
('Trade, Cotton',), ('Trade, NAFTA, Mexico',), ('Vegetarianism',),
('Veterans\x92 Benefits',), ('Violence',), ('Violence, Media,
Children',), ('Water',), ('Water, Privatization',), ('Welfare
Reform',), ('Wind, Renewable Energy',), ('Words',), ('World
Population',), ('World Bank',), ('Grassroots, Community, Living Wage,
Poverty',), ('Subsidies',), ('Protest, Action',), ('Nuclear Power
Plants, Emissions',), ('Media, Fear, Religion, George W. Bush',),
('Consciousness, Change',), ('Peace, Security, Oil',), ('Globalization,
Inequality, Commodity prices',), ('Water, Corporate',), ('Consumerism,
Developing Nations',), ('Nuclear Power, Wind Power',),
('Sustainability, Hope',), ('Ozone, CFCs, Chlorine, Atmosphere',),
('Hydrogen, Emissions, Ozone',), ('Water, Pesticides',), ('Iraq,
Military, Health',), ('Toxic Waste Reduction, Toxic Waste Disposal,
Environmental Regulation, Recycling Electronics',), ('Shareholder
Advocacy, Social Responsibility, Community Investing (Dem. Book)',),
('Fair Trade',), ('Genetically Modified Food, Africa, USAID, ISAAA',),
('Sustainability, Progress',), ('Sustainability, Human Rights,
Globalization, Corporatization',), ('Agricultural Subsidies,
Agricultural Policy',), ('Agricultural Subsidies, Farmers',), ('Cotton,
Rice',), ('Agricultural Subsidies, Farmers, Livestock',),
('Agricultural Subsidies, Prices',), ('Food scarcity, Hunger, Urban
Agriculture',), ('GMO farming, Toxic chemicals',), ('Hunger,
Children',), ('Hunger, Food Insecurity',), ('Inspiration, Corporatism,
War',), ('Inspiration, Economic Growth',), ('Land Vacancies, US Land
Reform',), ('Local agriculture, Local produce, Nutritional content of
local produce',), ('Local produce, Urban agriculture',), ('Pakistan,
Religion, Education',), ('Pharmaceuticals',), ('Privatization',),
('Public panic, Altruism',), ('QA rewrite: citizen activism,
corporatism-environment, hope, inspiration',), ('Wal-Mart, Food
Stamps',), ('Socially Responsible Investment, Banking and Environmental
Initiatives',), ('Terrorism, Suicide Attacks',), ('Agriculture,
Corporate v. Family Farming',), ('Depression, Health',), ('Depression,
Health, Employment, Work, Employee',), ('Self-Interest, Vote,
Altruism',), ('Animal Feed',), ('Corporate Reform (Dem. Book)',),
('Economic Growth',), ('Corporate History, Globalization',),
('Agriculture',), ('Environment, Public Opinion (Dem. Book)',),
('Illiteracy, poverty',), ('Military, Arms Sales',), ('Agriculture;
Agricultural imports; Global trade',), ('Africa; Foreign aid; Third
World debt; Loan forgiveness; Jubilee',), ('Foreign aid; Military aid;
Development aid; Humanitarian aid',), ('Foreign aid; Development
assistance',), ('Agricultural Subsidies; Foreign aid; Cotton subsidies;
Africa',))

>>

[Fredrik Lundh]

ronrsr wrote:

it looks like the len() function is the one I want.

for: len(result) - i get 248,

but for len(result[0]) or len(result[1]) i always get 0.

that's a bit surprising, because both items are tuples that contain
exactly one item:

>(('Agricultural subsidies; Foreign aid',), ('Agriculture; Sustainable
Agriculture - Support; Organic Agriculture; Pesticides, US, Childhood
Development, Birth Defects; Toxic Chemicals',),

</F>

[ronrsr]

very sorry, that was my error - len(result[0]) and len(result[1]) both
return 1 --

i think I'm misunderstanding what len() does - to me they appear to
have 2 or 3 elements, or at least be composed of a string of some
length.

I guess len() isn't the function i'm looking for then. How do I tell
how many strings, or how many bytes are in each dimension? so that I
can iterate through them.

Each entry in that list is a keyword - Alternately, is there any fast
way to parse that into a sorted list of distinct keywords.

very sorry for the error.

bests,

-rsr-

Fredrik Lundh wrote:

ronrsr wrote:

it looks like the len() function is the one I want.

for: len(result) - i get 248,

but for len(result[0]) or len(result[1]) i always get 0.

that's a bit surprising, because both items are tuples that contain
exactly one item:

(('Agricultural subsidies; Foreign aid',), ('Agriculture; Sustainable
Agriculture - Support; Organic Agriculture; Pesticides, US, Childhood
Development, Birth Defects; Toxic Chemicals',),

</F>

[John Machin]

ronrsr top-posted [uncorrected]:

very sorry, that was my error - len(result[0]) and len(result[1]) both
return 1 --

i think I'm misunderstanding what len() does - to me they appear to
have 2 or 3 elements, or at least be composed of a string of some
length.

len(result) is 248 because you have retrieved 248 rows from the
database.

len(result[0]), len(result[1]), etc etc are all each 1 because you have
selected only one column from each row -- I'm guessing here because you
have not supplied the SQL query [that would have been much better than
the last 200 rows of result!]

To get the one column out, you need to select the first (and only!)
column from each row.

result[0][0]
result[1][0]
etc

>
I guess len() isn't the function i'm looking for then. How do I tell
how many strings, or how many bytes are in each dimension? so that I
can iterate through them.

len() *is* the function that you are looking for. The problem that
needs to be solved before we can help you is that we don't understand
your data. Stripped of all the Python "punctuation", what you would see
if you used some user-interface tool [I'm not familiar with mysql] to
"print" each row to your screen *might* be something like this [it
would number lines from 1 but we'll do it from 0 to avoid doubling the
confusion]

0: Agricultural subsidies; Foreign aid
1: Agriculture; Sustainable Agriculture - Support; Organic Agriculture;
Pesticides, US, Childhood Development, Birth Defects; Toxic Chemicals
2: Antibiotics, Animals
3: Agricultural Subsidies, Global Trade
4: Agricultural Subsidies
5: Biodiversity
etc

Looks like each line contains one or more topics separated by
semicolons. E.g. in line 0,
"Agricultural subsidies" and "Foreign aid" are 2 topics. Line 1
contains 5 topics.

Note carefully:
[1] Line 4 has 1 topic; it is essentially the *same* topic as the first
topic in line 0 but note "S" versus "s"
[2] Either Line 3 has 1 topic which is a *subtopic* of "Agricultural
Subsidies" *OR* the comma should be a semi-colon [so that it would have
2 topics, very similar to line 0] -- in any case, this is a
complication ....

>
Each entry in that list is a keyword

What do you mean by "entry" and "list" and "keyword"? Give examples.

- Alternately, is there any fast

way to parse that into a sorted list of distinct keywords.

Quite possibly, if only you could say what you mean by "keywords".
Please give a *small* example, like what you would expect from the
first 6 lines as quoted above.

Multi-topic lines can be split up by using line.split(";") -- but
understanding (at least partial) should come before coding, IMHO.

HTH,
John

Fredrik Lundh wrote:

ronrsr wrote:

it looks like the len() function is the one I want.
>
for: len(result) - i get 248,
>
but for len(result[0]) or len(result[1]) i always get 0.

that's a bit surprising, because both items are tuples that contain
exactly one item:

[Fredrik Lundh]

ronrsr wrote:

very sorry, that was my error - len(result[0]) and len(result[1]) both
return 1 --

i think I'm misunderstanding what len() does - to me they appear to
have 2 or 3 elements, or at least be composed of a string of some
length.

from python's perspective, the data structure you're looking at looks
like

tuple of
tuple of
one string
tuple of
one string
tuple of
one string
etc

so len(result) is the number of tuples, and len(result[index]) is the
number of strings in the inner tuples (=1). to get at an individual
string, do

s = result[index][0]

to look inside the string, you need to apply the appropriate string
operations to extract the data, or split it up in some suitable way.
there's no way Python can figure out how a string appears to you; from
Python's perspective, it's just a bunch of characters.

</F>

[mensanator]

ronrsr wrote:

very sorry, that was my error - len(result[0]) and len(result[1]) both
return 1 --

i think I'm misunderstanding what len() does - to me they appear to
have 2 or 3 elements, or at least be composed of a string of some
length.

One string composed of multiple data elements means
the problem is in your database design, not in your Python code.

>
I guess len() isn't the function i'm looking for then. How do I tell
how many strings, or how many bytes are in each dimension?

You have to split the strings based on some delimiter, but your
delimiters seem inconsistent. Unless the different delimiters actually
have different meanings which, again, implies bad database design.

so that I
can iterate through them.

Each entry in that list is a keyword - Alternately, is there any fast
way to parse that into a sorted list of distinct keywords.

That could easily be done in the database itself, but only if it is
designed properly.

>
very sorry for the error.

bests,

-rsr-

Fredrik Lundh wrote:

ronrsr wrote:

it looks like the len() function is the one I want.
>
for: len(result) - i get 248,
>
but for len(result[0]) or len(result[1]) i always get 0.

that's a bit surprising, because both items are tuples that contain
exactly one item:

>(('Agricultural subsidies; Foreign aid',), ('Agriculture; Sustainable
>Agriculture - Support; Organic Agriculture; Pesticides, US, Childhood
>Development, Birth Defects; Toxic Chemicals',),

</F>