Sorting Rows Alphabetically

Sort each row of character strings alphabetically in R

With dplyr, you can try:

df %>%
 rowwise() %>%
 mutate(names = paste(sort(unlist(strsplit(names, ", ", fixed = TRUE))), collapse = ", "))

  names                       var1  var2
  <chr>                      <dbl> <dbl>
1 John D., Josh C., Karl H. -0.226  19.9
2 Bob S., John D., Tim H.    0.424  24.8
3 Amy A., Art U., Wes T.     1.42   25.0
4 John D., Josh C., Karl H.  5.42   20.4

Sample data:

df <- data.frame(names, var1, var2,
                 stringsAsFactors = FALSE)

Python order dataframe alphabetically

Pre pandas 0.17:

# Sort by ascending student name
df.sort('student')
# reverse ascending
df.sort('student', ascending=False)

Pandas 0.17+ (as mentioned in the other answers):

# ascending
df.sort_values('student')
# reverse ascending
df.sort_values('student', ascending=False)

How can I sort csv data alphabetically then numerically by column?

The following assumes bash (if you don't use bash replace $'\t' by a quoted real tab character) and GNU coreutils. It also assumes that you want to sort alphabetically by Make column first, then numerically in decreasing order by Total, and finally keep at most the first 3 of each Make entries.

Sorting is a job for sort, head and tail can be used to isolate the header line, and awk can be used to keep maximum 3 of each Make, and re-number the first column:

$ head -n1 data.tsv; tail -n+2 data.tsv | sort -t$'\t' -k4,4 -k6,6rn |
  awk -F'\t' -vOFS='\t' '$4==p {n+=1} $4!=p {n=1;p=$4} {$1=++r} n<=3'
Ranking  ID   Year  Make        Model           Total
1        113  2012  Acura       Tsx sportwagon  116
2        112  2008  Acura       TL              110
3        50   2015  Acura       TLX             102
4        15   014   Audi        S4              120
5        216  2007  Chrystler   300             96
6        83   2014  Honda       Accord          112
7        65   2009  Honda       Fit             106
8        31   2007  Honda       Fit             102
10       128  2010  Infiniti    G37             128
11       124  2015  Jeep        Wrangler        124
12       91   2010  Mitsu       Lancer          102
13       126  2010  Volkswagen  Eos             92

Note that this is different from your expected output: Make is sorted in alphabetic order (Audi comes after Acura, not Honda) and only the 3 largest Total are kept (112, 106, 102 for Honda, not 112, 102, 92).

If you use GNU awk, and your input file is small enough to fit in memory, you can also do all this with just awk, thanks to its multidimensional arrays and its asorti function, that sorts arrays based on indices:

$ awk -F'\t' -vOFS='\t' 'NR==1 {print; next} {l[$4][$6][$0]}
  END {
    PROCINFO["sorted_in"] = "@ind_str_asc"
    for(m in l) {
      n = asorti(l[m], t, "@ind_num_desc"); n = (n>3) ? 3 : n
      for(i=1; i<=n; i++) for(s in l[m][t[i]]) {$0 = s; $1 = ++r; print}
    }
  }' data.tsv
Ranking  ID   Year  Make        Model           Total
1        113  2012  Acura       Tsx sportwagon  116
2        112  2008  Acura       TL              110
3        50   2015  Acura       TLX             102
4        15   014   Audi        S4              120
5        216  2007  Chrystler   300             96
6        83   2014  Honda       Accord          112
7        65   2009  Honda       Fit             106
8        31   2007  Honda       Fit             102
9        128  2010  Infiniti    G37             128
10       124  2015  Jeep        Wrangler        124
11       91   2010  Mitsu       Lancer          102
12       126  2010  Volkswagen  Eos             92

Sorting rows into non-alphabetical customized order

We can do this in base R

i1 <- with(df, ave(seq_len(nrow(df)), as.integer(gl(nrow(df), 3, 
         nrow(df))), FUN = function(i) c(i[c(2, 1, 3)])))
out <- df[i1,]
row.names(out) <- NULL
out
#    Name Code
#1    Gas    2
#2    Tax    1
#3    Gas    2
#4    Gas    2
#5    Tax    1
#6    Gas    2
#7  Lunch    2
#8    Tax    1
#9  Lunch    2
#10   Car    2
#11   Tax    1
#12   Car    2

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Or with tidyverse

library(tidyverse)
df %>% # initial dataset
   uncount(Code, .remove = FALSE) %>%
   mutate(rn = row_number()) %>% 
   group_by(grp = gl(n(), 3, n())) %>%
   slice(c(2, 1, 3)) %>% 
   ungroup %>% 
   select(-rn, -grp)

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