Cryzzly

Crystal library for data analysis/manipulation. Inspired by Pandas

Installation

1. Add the dependency to your shard.yml:

   dependencies:
     cryzzly:
       github: franciscoGPS/cryzzly
   

2. Run shards install

Usage

require "cryzzly"


# Load CSV file 
df = Cryzzly::DataFrame(Any).load_data("my_csv.csv", index_col: 0, index_type: "datetime" )

# From matrix array
df = Dataframe(Float64).new([[1,2,3], [4,5,6]], ["col_1","col_2","col_3"])


# Return headers list
df.headers

# Sum of specified column keys
df.sum("col_1","col_2", "col_3")

# Mean of specified column keys
df.mean("col_1","col_2", "col_3")

# Amount of columns 
df.length

# Shape of dataset
df.shape

# Minimum value of specified column keys 
df.min("col_1","col_2", "col_3")  
 
# Maximum value of specified column keys
df.max("col_1","col_2", "col_3")
 
# Standard deviation
df.std("col_1","col_2", "col_3")

# Uses Aquaplot to plot files. Stored on specified location

df.plot("col_1","col_2", filename: "my_plot")
## Output "Image stored in: my_plot.png"


df.to_csv("col_1","col_2", "col_3", filename: "my_csv")
## Output "File stored in: my_csv.csv"

TODO:

String and timestamp column analysis

• [] count
• [] unique
• [] top
• [] freq

Contributing

1. Fork it (https://github.com/your-github-user/cryzzly/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

• Francisco C. - Creator and maintainer