df$count <- rowSums(df[-1] == “NC”) # V1 V2 V3 V4 count # 1 rs1 NC AB NC 2 # 2 rs2 AB NC AA 1 # 3 rs3 NC NC NC 3 We can use rowSums on the matrix that is created from this expression df[-1] == “NC”.
In Python 2.7 and above there is a class called Counter which can help you: from collections import Counter words_to_count = (word for word in word_list if word[:1].isupper()) c = Counter(words_to_count) print c.most_common(3) Result: [(‘Jellicle’, 6), (‘Cats’, 5), (‘And’, 2)] I am quite new to programming so please try and do it in the most … Read more
Performance comparison Note: time in the table doesn’t include the time it takes to load files. | approach | american-english, | big.txt, | time w.r.t. defaultdict | | | time, seconds | time, seconds | | |—————-+——————-+—————+————————-| | Counter | 0.451 | 3.367 | 3.6 | | setdefault | 0.348 | 2.320 | 2.5 | … Read more
The Counter class in the collections module is purpose built to solve this type of problem: from collections import Counter words = “apple banana apple strawberry banana lemon” Counter(words.split()) # Counter({‘apple’: 2, ‘banana’: 2, ‘strawberry’: 1, ‘lemon’: 1})
In Python 2.7 (or newer), you can use collections.Counter: import collections a = [1,1,1,1,2,2,2,2,3,3,4,5,5] counter=collections.Counter(a) print(counter) # Counter({1: 4, 2: 4, 3: 2, 5: 2, 4: 1}) print(counter.values()) # [4, 4, 2, 1, 2] print(counter.keys()) # [1, 2, 3, 4, 5] print(counter.most_common(3)) # [(1, 4), (2, 4), (3, 2)] print(dict(counter)) # {1: 4, 2: 4, … Read more
I’m not really sure what you used, but table works fine for me! Here’s a minimal reproducible example: df <- structure(list(V1 = c(“a”, “a”, “a”, “b”, “b”, “b”, “c”), V2 = c(“g”, “h”, “g”, “i”, “g”, “h”, “i”)), .Names = c(“V1”, “V2”), class = “data.frame”, row.names = c(NA, -7L)) table(df) # V2 # V1 g … Read more
use this from collections import Counter list1=[‘apple’,’egg’,’apple’,’banana’,’egg’,’apple’] counts = Counter(list1) print(counts) # Counter({‘apple’: 3, ‘egg’: 2, ‘banana’: 1})
Try this: mtcars %>% group_by(am, gear) %>% summarise(n = n()) %>% mutate(freq = n / sum(n)) # am gear n freq # 1 0 3 15 0.7894737 # 2 0 4 4 0.2105263 # 3 1 4 8 0.6153846 # 4 1 5 5 0.3846154 From the dplyr vignette: When you group by multiple variables, … Read more
Use groupby and count: In [37]: df = pd.DataFrame({‘a’:list(‘abssbab’)}) df.groupby(‘a’).count() Out[37]: a a a 2 b 3 s 2 [3 rows x 1 columns] See the online docs: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html Also value_counts() as @DSM has commented, many ways to skin a cat here In [38]: df[‘a’].value_counts() Out[38]: b 3 a 2 s 2 dtype: int64 If … Read more
I guess Android native does not have API for FM Radio. No open API. Third party apps have their own-custom API’s built. Check these threads for more info: “How to set a frequency for the fm radio in android?“ A radio API for Android FM radio application in android development How to make an FM … Read more