In a dataframe like this:
...
match team opponent venue
233 3b0345fb Brazil Argentina Home
234 3b2357fb Argentina Brazil Away
427 3b0947fb England Poland Home
...
how can I select one dataframe slice, based on a column value (df[df['team']=='England']), like this:
...
match team opponent venue
559 4a3eae2f England Poland Home
...
And add inverted rows of that slice to the original dataframe, changing 'Home' with 'Away', ending up with:
...
match team opponent venue
233 3b0345fb Brazil Argentina Home
234 3b2357fb Argentina Brazil Away
559 3b0947fb England Poland Home
560 3b0947fb Poland England Away
...
Note: This slice should contain n rows and produce n inverted rows.
You can use:
df2 = df[df['team'].eq('England')].copy()
df2[['team', 'opponent']] = df2[['opponent', 'team']]
df2['venue'] = df2['venue'].map({'Home': 'Away', 'Away': 'Home})
out = pd.concat([df, df2])
print(out)
Output:
match team opponent venue
233 3b0345fb Brazil Argentina Home
234 3b2357fb Argentina Brazil Away
427 3b0947fb England Poland Home
427 3b0947fb Poland England Away
If you want to invert all:
df2 = df.copy()
df2[['team', 'opponent']] = df2[['opponent', 'team']]
df2['venue'] = df2['venue'].map({'Home': 'Away', 'Away': 'Home})
out = pd.concat([df, df2])
output:
match team opponent venue
233 3b0345fb Brazil Argentina Home
234 3b2357fb Argentina Brazil Away
427 3b0947fb England Poland Home
233 3b0345fb Argentina Brazil Away
234 3b2357fb Brazil Argentina Home
427 3b0947fb Poland England Away
Related
I pretty new to this,
What I am trying to accomplished is having a table with distrcits and their various neighborhoods but my final code just list all neighborhoods in a list format without assigning them to a specific district.
url = "https://en.wikipedia.org/wiki/List_of_neighbourhoods_in_Toronto"
html = urlopen(url)
soup = BeautifulSoup(html, 'lxml')
type(soup)
print(soup.prettify())
Toronto_table = soup.find('table',{'class':'wikitable sortable'})
links = Toronto_table.find_all('a')
neighborhoods = []
for link in links:
neighborhoods.append(link.get('title'))
print(neighborhoods)
df_neighborhoods = pd.DataFrame(neighborhoods)
df_neighborhoods
You can simply read_html and print the table.
import pandas as pd
f_states=pd.read_html('https://en.wikipedia.org/wiki/List_of_neighbourhoods_in_Toronto')
print(f_states[6])
Output :
District Number Neighbourhoods Included
0 C01 Downtown, Harbourfront, Little Italy, Little P...
1 C02 The Annex, Yorkville, South Hill, Summerhill, ...
2 C03 Forest Hill South, Oakwood–Vaughan, Humewood–C...
3 C04 Bedford Park, Lawrence Manor, North Toronto, F...
4 C06 North York, Clanton Park, Bathurst Manor
5 C07 Willowdale, Newtonbrook West, Westminster–Bran...
6 C08 Cabbagetown, St. Lawrence Market, Toronto wate...
7 C09 Moore Park, Rosedale
8 C10 Davisville Village, Midtown Toronto, Lawrence ...
9 C11 Leaside, Thorncliffe Park, Flemingdon Park
10 C13 Don Mills, Parkwoods–Donalda, Victoria Village
11 C14 Newtonbrook East, Willowdale East
12 C15 Hillcrest Village, Bayview Woods – Steeles, Ba...
13 E01 Riverdale, Danforth (Greektown), Leslieville
14 E02 The Beaches, Woodbine Corridor
15 E03 Danforth (Greektown), East York, Playter Estat...
16 E04 The Golden Mile, Dorset Park, Wexford, Maryval...
17 E05 Steeles, L'Amoreaux, Tam O'Shanter – Sullivan
18 E06 Birch Cliff, Oakridge, Hunt Club, Cliffside
19 E08 Scarborough Village, Cliffcrest, Guildwood, Eg...
20 E09 Scarborough City Centre, Woburn, Morningside, ...
21 E10 Rouge (South), Port Union (Centennial Scarboro...
22 E11 Rouge (West), Malvern
23 W01 High Park, South Parkdale, Swansea, Roncesvall...
24 W02 Bloor West Village, Baby Point, The Junction (...
25 W03 Keelesdale, Eglinton West, Rockcliffe–Smythe, ...
26 W04 York, Glen Park, Amesbury (Brookhaven), Pelmo ...
27 W05 Downsview, Humber Summit, Humbermede (Emery), ...
28 W06 New Toronto, Long Branch, Mimico, Alderwood
29 W07 Sunnylea (The Queensway – Humber Bay)
30 W08 The Kingsway, Central Etobicoke, Eringate – Ce...
31 W09 Kingsview Village-The Westway, Richview (Willo...
32 W10 Rexdale, Clairville, Thistletown - Beaumond He...
Look at the following data file（cou.data） which has four fields separated by tab.
Four fields are:
country name
land area
population
continent
As for country name or continent name which has two words, two words are separated by space.
(Data are not accurately confirmed, just for test purpose)
USSR 8649 275 Asia
Cananda 3852 25 North America
China 3705 1032 Asia
USA 3615 237 North America
Brazil 3286 134 South America
India 1267 746 Asia
Mexico 762 78 North America
France 211 55 Europe
Japan 144 120 Asia
Germany 96 61 Europe
England 94 56 Europe
Taiwan 55 144 Asia
North Korea 44 2134 Asia
awk 'BEGIN { FS = "\t" } { print $1, "---", $4 }' cou.data
I got the output which exactly meets my anticipation:
USSR --- Asia
Cananda --- North America
China --- Asia
USA --- North America
Brazil --- South America
India --- Asia
Mexico --- North America
France --- Europe
Japan --- Asia
Germany --- Europe
England --- Europe
Taiwan --- Asia
North Korea --- Asia
Then I replace \t by one space (" ")
That is :
awk 'BEGIN { FS = " " } { print $1, "---", $4 }' cou.data
The output I got is not understandable to me
USSR --- Asia
Cananda --- North
China --- Asia
USA --- North
Brazil --- South
India --- Asia
Mexico --- North
France --- Europe
Japan --- Asia
Germany --- Europe
England --- Europe
Taiwan --- Asia
North --- 2134
Line 2,4,5,7,13 each have one space and the other lines have no space(s) at all.
As for lines that have no space, why $1, $4 still can be printed ?
As for line 2,4,5,7,13, I thought $1 should be printed like this:
Cananda 3852 25 North
USA 3615 237 North
Brazil 3286 134 South
Mexico 762 78 North
North
And $4 does not exist.
Where did I get wrong ?
So problem here is string/country names on 1st field which are having spaces in their names for example North Korea. So when you are setting FS as \t this string will be considered as a single field on the other hand when you will set FS as space this will be considered as 2 different fields. That is why you are seeing difference between field numbers after changing the FS values in your codes.
I would suggest your first attempt is good enough to get your expected values.
I am trying to understand how the range pattern matching work in Awk
Here is the full data that I am practicing with
Raw Data
-----------------------------------------
USSR 8649 275 Asia
Canada 3852 25 North America
China 3705 1032 Asia
USA 3615 237 North America
Brazil 3286 134 South America
India 1267 746 Asia
Mexico 762 78 North America
France 211 55 Europe
Japan 144 120 Asia
Germany 96 61 Europe
England 94 56 Europe
If I write this code
$ awk '/Asia/, /Europe/' countries.awk
I get
USSR 8649 275 Asia
Canada 3852 25 North America
China 3705 1032 Asia
USA 3615 237 North America
Brazil 3286 134 South America
India 1267 746 Asia
Mexico 762 78 North America
France 211 55 Europe
Japan 144 120 Asia
Germany 96 61 Europe
It doesn't output England.
And If I write this
$ awk '/Europe/, /Asia/' countries.awk
I get
France 211 55 Europe
Japan 144 120 Asia
Germany 96 61 Europe
England 94 56 Europe
What is the behavior here? Why do I not get England on the first one?
Awk process input lines one at a time, the syntax you used is likely to print lines from the start to the end pattern, represented by country names. When you used
awk '/Asia/, /Europe/'
The start of pattern Asia happens more than once. As you can see from the line numbers below, numbers 3,5,8 and 11 represent the start of the pattern and the pattern ends at lines 10 and 12. Observe carefully the sub-ranges of lines between 8-10 and 11-12. The last end pattern Europe for the last Asia ends at line 12, that is the reason you are not seeing England in the first case.
But when you used
awk '/Europe/, /Asia/'
The line containing the first start pattern Europe starts at line 10 and ends at 11 another two pattern start at 12 and 13 without an end pattern Asia, so it would obviously print all the lines until Asia appears. So you are seeing England in the second case.
$ cat -n file
1 Raw Data
2 -----------------------------------------
3 USSR 8649 275 Asia
4 Canada 3852 25 North America
5 China 3705 1032 Asia
6 USA 3615 237 North America
7 Brazil 3286 134 South America
8 India 1267 746 Asia
9 Mexico 762 78 North America
10 France 211 55 Europe
11 Japan 144 120 Asia
12 Germany 96 61 Europe
13 England 94 56 Europe
Never use range expressions as they make trivial tasks very slightly briefer but then need a complete rewrite or duplicate conditions when your requirements change. Always use a flag instead:
awk '/Asia/{f=1} f{print} /Europe/{f=0}' countries.awk
I bet if you started with that you wouldn't even have had to ask this question as the logic is clear and explicit.
Given this code in iPython
df1=df["BillingContactCountry"].value_counts()
df1
I get
United States 4138
Germany 1963
United Kingdom 732
Switzerland 528
Australia 459
Canada 369
Japan 344
France 303
Netherlands 285
I want to get a series with count larger than 303, what should I do?
You needboolean indexing:
print (df1[df1 > 303])
United States 4138
Germany 1963
United Kingdom 732
Switzerland 528
Australia 459
Canada 369
Japan 344
Name: BillingContactCountry, dtype: int64
Long time reader, first time poster.
I'm trying to consolidate a table I have to the rate of sold goods getting lost in transit. In this table, we have four kinds of products, three countries of origin, three transit countries (where the goods are first shipped to before being passed to customers) and three destination countries. The table is as follows.
Status Product Count Origin Transit Destination
--------------------------------------------------------------------
Delivered Shoes 100 Germany France USA
Delivered Books 50 Germany France USA
Delivered Jackets 75 Germany France USA
Delivered DVDS 30 Germany France USA
Not Delivered Shoes 7 Germany France USA
Not Delivered Books 3 Germany France USA
Not Delivered Jackets 5 Germany France USA
Not Delivered DVDS 1 Germany France USA
Delivered Shoes 300 Poland Netherlands Canada
Delivered Books 80 Poland Netherlands Canada
Delivered Jackets 25 Poland Netherlands Canada
Delivered DVDS 90 Poland Netherlands Canada
Not Delivered Shoes 17 Poland Netherlands Canada
Not Delivered Books 13 Poland Netherlands Canada
Not Delivered Jackets 1 Poland Netherlands Canada
Delivered Shoes 250 Spain Ireland UK
Delivered Books 20 Spain Ireland UK
Delivered Jackets 150 Spain Ireland UK
Delivered DVDS 60 Spain Ireland UK
Not Delivered Shoes 19 Spain Ireland UK
Not Delivered Books 8 Spain Ireland UK
Not Delivered Jackets 8 Spain Ireland UK
Not Delivered DVDS 10 Spain Ireland UK
I would like to create a new table that shows the count of goods delivered and not delivered in one row, like this.
Product Delivered Not_Delivered Origin Transit Destination
Shoes 100 7 Germany France USA
Books 50 3 Germany France USA
Jackets 75 5 Germany France USA
DVDS 30 1 Germany France USA
Shoes 300 17 Poland Netherlands Canada
Books 80 13 Poland Netherlands Canada
Jackets 25 1 Poland Netherlands Canada
DVDS 90 0 Poland Netherlands Canada
Shoes 250 19 Spain Ireland UK
Books 20 8 Spain Ireland UK
Jackets 150 8 Spain Ireland UK
DVDS 60 10 Spain Ireland UK
I've had a look at some other posts and so far I haven't found exactly what I'm looking for. Perhaps the issue here is that there will be multiple WHERE statements in the code to ensure that I don't group all shoes together, ore all country groups.
Is this possible with SQL?
Something like this?
select
product
,sum(case when status = 'Delivered' then count else 0 end) as delivered
,sum(case when status = 'Not Delivered' then count else 0 end) as not_delivered
,origin
,transit
,destination
from table
group by
product
,origin
,transit
,destination
This is rather easy; instead of one line per Product, Origin, Transit, Destination and Status, you want one result line per Product, Origin, Transit and Destination only. So group by these four columns and aggregate conditionally:
select
product, origin, transit, destination,
sum(case when status = 'Delivered' then "count" else 0 end) as delivered,
sum(case when status = 'Not Delivered' then "count" else 0 end) as not_delivered
from mytable
group by product, origin, transit, destination;
BTW: It is not a good idea to use a keyword for a column name. I used double quotes to use your column count, which is standard SQL, but I don't know if it works in Google BigQuery. Maybe it must be "Count" rather than "count" or something entirely else.)
SELECT
product, origin, transit, destination,
SUM([count] * (status = 'Delivered')) AS delivered,
SUM([count] * (status = 'Not Delivered')) AS not_delivered
FROM mytable
GROUP BY 1, 2, 3, 4