tinypandas

TODO: Write a description here

Installation

1. Add the dependency to your shard.yml:

   dependencies:
     tinypandas:
       github: orangeSi/tinypandas
   

2. Run shards install

Features

1. support seprated by tab format or csv or vcf format file

Usage

test code is in example/test.cr like this:

require "tinypandas"

pd = Tinypandas.new

## support seprate by tab format file
df = pd.read_table(ifile, sep: "\t") # def read_table(filepath_or_buffer : String, sep = "\t", delimiter : String = "\n", header : HeaderType = 0, index_col : IndexColType = 0, comment : String|Regex = "#", skiprows : SkiprowsType = false, skip_blank_lines : Bool = true)

puts "df is #{df}\n"

puts "df.to_str is\n#{df.to_str}\n"

puts "df[A2][B3] is #{df["A2"]["B3"]}\n"

puts "df[df[A2]>=5].to_str is"
puts df[df["A2"]>=5].to_str

puts "df[df[A3]==9][A2].to_str is "
puts df[df["A3"]==9]["A2"].to_str

puts "df[df[A3]>=3][A2].to_str is "
puts df[df["A3"]>=3]["A2"].to_str

t = df["A2"]
puts "t = df[A2]is #{t}"
puts "t>2 is #{t>2}"

puts "df.t.to_str is\n#{df.t.to_str}"

puts "df.t[B3][A1] is "
puts df.t["B3"]["A1"]


## support vcf format file
df = pd.load_vcf("demo.vcf")
puts "df.head(1).to_s is\n"
puts df.head(1).to_s
puts "\n"

## support csv format file
df = pd.load_csv("sample.csv")
puts "df is #{df}\n"
puts "df.to_str is\n#{df.to_str}\n"
puts "df[col2][2] is #{df["col2"]["2"]}\n"


## convert Array(Array) to DataFrame
data = [[1,2,3],[4,5,6],[6,7,8]]
df = DataFrame.new(data, columns: ["c1","c2","c3"]) # read_array_by_row: true
puts "\nArray(Array()):#{data} to DataFrame:\n#{df.to_s}"

## read Hash(String, Array()) as DataFrame
data = {"c1"=>[1,2,3], "c2"=>[4,5,6], "c3"=>[6,7,8]}
df = DataFrame.new(data)
puts "\nHash(String, Array()):#{data} to DataFrame:\n#{df.to_s}"

then go to example cd example; crystal build test.cr --release

$cat demo.xls
# note
	A1	A3	A2
B1	1	3	2
B2	7	2	8
B3	4	9	5

then ./test demo.xls or ./test demo.xls.gz will get this:

## support seprate by tab format file
intpu file demo.xls

df is DataFrame(@dict={"A1" => Series(@dict={"B1" => 1, "B2" => 7, "B3" => 4}), "A3" => Series(@dict={"B1" => 3, "B2" => 2, "B3" => 9}), "A2" => Series(@dict={"B1" => 2, "B2" => 8, "B3" => 5})}, @index=["B1", "B2", "B3"], @columns=["A1", "A3", "A2"])

df.to_str is
	A1	A3	A2
B1	1	3	2
B2	7	2	8
B3	4	9	5

df[A2][B3] is 5
df[df[A2]>=5].to_str is
	A1	A3	A2
B2	7	2	8
B3	4	9	5

df[df[A3]==9][A2].to_str is 
B3	5

df[df[A3]>=3][A2].to_str is 
B1	2
B3	5
t = df[A2]is Series(@dict={"B1" => 2, "B2" => 8, "B3" => 5})
t>2 is Series(@dict={"B2" => 8, "B3" => 5})

df.t.to_str is
	B1	B2	B3
A1	1	7	4
A3	3	2	9
A2	2	8	5

df.t[B3][A1] is 
4

## support vcf format file
df.head(1).to_s is
	#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	HG00096	HG00097	HG00099
0	MT	10	.	T	C	100	fa	VT=S;AC=3	GT	0	0	0

## support csv format file
df is DataFrame(@dict={"date" => Series(@dict={"0" => "2020-02-01 12:00:02", "1" => "2020-02-01 12:00:07", "2" => "2020-02-01 12:00:12", "3" => "2020-02-01 12:00:17", "4" => "2020-02-01 12:00:22", "5" => "2020-02-01 12:00:27", "6" => "2020-02-01 12:00:32", "7" => "2020-02-01 12:00:37"}), "col1" => Series(@dict={"0" => 66808, "1" => 66873, "2" => 66875, "3" => 66874, "4" => 66881, "5" => 66858, "6" => 66905, "7" => 66885}), "col2" => Series(@dict={"0" => 0.68, "1" => 0.67, "2" => 0.65, "3" => 0.67, "4" => 0.67, "5" => 0.66, "6" => 0.64, "7" => 0.66}), "col3" => Series(@dict={"0" => "TRUE", "1" => "FALSE", "2" => "TRUE", "3" => "FALSE", "4" => "TRUE", "5" => "FALSE", "6" => "TRUE", "7" => "FALSE"}), "col4" => Series(@dict={"0" => "str1", "1" => "str2", "2" => "str3", "3" => "str4", "4" => "str5", "5" => "str6", "6" => "str7", "7" => "str8"})}, @index=["0", "1", "2", "3", "4", "5", "6", "7"], @columns=["date", "col1", "col2", "col3", "col4"])
df.to_str is
	date	col1	col2	col3	col4
0	2020-02-01 12:00:02	66808	0.68	TRUE	str1
1	2020-02-01 12:00:07	66873	0.67	FALSE	str2
2	2020-02-01 12:00:12	66875	0.65	TRUE	str3
3	2020-02-01 12:00:17	66874	0.67	FALSE	str4
4	2020-02-01 12:00:22	66881	0.67	TRUE	str5
5	2020-02-01 12:00:27	66858	0.66	FALSE	str6
6	2020-02-01 12:00:32	66905	0.64	TRUE	str7
7	2020-02-01 12:00:37	66885	0.66	FALSE	str8

df[col2][2] is 0.65

Array(Array()):[[1, 2, 3], [4, 5, 6], [6, 7, 8]] to DataFrame:
	c1	c2	c3
0	1	2	3
1	4	5	6
2	6	7	8

Hash(String, Array()):{"c1" => [1, 2, 3], "c2" => [4, 5, 6], "c3" => [6, 7, 8]} to DataFrame:
	c1	c2	c3
0	1	4	6
1	2	5	7
2	3	6	8

TODO: Write usage instructions here

Development

TODO: Write development instructions here

Contributing

1. Fork it (https://github.com/orangeSi/tinypandas/fork)
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request

Contributors

• orangeSi - creator and maintainer