neat-panda 0.6.5

Neat Panda contains functions written to mimic the R package Tidyr.

# Neat Panda

[](https://pypi.python.org/pypi/neat_panda)

[](https://travis-ci.org/htp84/neat_panda)

[](https://codecov.io/gh/htp84/neat-panda)

Neat Panda contains two functions, spread and gather. These functions and its parameters are written to mimic the spread and gather functions in the R package [*tidyr*](https://tidyr.tidyverse.org/). This is done using the [*pandas*](https://pandas.pydata.org/pandas-docs/stable/) library methods [*pivot*](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html) and [*melt*](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.melt.html).

## Features
### Spread
#### R
```R
library(tidyr)
library(dplyr)
library(gapminder)

gapminder2 <- gapminder %>% select(country, continent, year, pop)
gapminder3 <- gapminder2 %>% spread(key=year, value=pop)
head(gapminder3, n = 5)
```
#### Python
```python
from neat_panda import spread
from gapminder import gapminder

gapminder2 = gapminder[["country", "continent", "year", "pop"]]
gapminder3 = spread(df=gapminder2, key="year", value="pop")
# or
gapminder3 = gapminder2.pipe(spread, key="year", value="pop")

gapminder3.head()
```
##### Output R
```
# A tibble: 5 x 14
country continent `1952` `1957` `1962` `1967` `1972` `1977` `1982` `1987` `1992` `1997` `2002` `2007`
<fct> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 Afghanistan Asia 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 25268405 31889923
2 Albania Europe 1282697 1476505 1728137 1984060 2263554 2509048 2780097 3075321 3326498 3428038 3508512 3600523
3 Algeria Africa 9279525 10270856 11000948 12760499 14760787 17152804 20033753 23254956 26298373 29072015 31287142 33333216
4 Angola Africa 4232095 4561361 4826015 5247469 5894858 6162675 7016384 7874230 8735988 9875024 10866106 12420476
5 Argentina Americas 17876956 19610538 21283783 22934225 24779799 26983828 29341374 31620918 33958947 36203463 38331121 40301927
```
##### Output Python
```
country continent 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
0 Afghanistan Asia 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 25268405 31889923
1 Albania Europe 1282697 1476505 1728137 1984060 2263554 2509048 2780097 3075321 3326498 3428038 3508512 3600523
2 Algeria Africa 9279525 10270856 11000948 12760499 14760787 17152804 20033753 23254956 26298373 29072015 31287142 33333216
3 Angola Africa 4232095 4561361 4826015 5247469 5894858 6162675 7016384 7874230 8735988 9875024 10866106 12420476
4 Argentina Americas 17876956 19610538 21283783 22934225 24779799 26983828 29341374 31620918 33958947 36203463 38331121 40301927
```


### Gather
#### R
```R
library(tidyr)
# gapminder3 is obtained as above
gapminder4 <- gather(gapminder3, key="year", "value"="pop", 3:14)
# or
years <- c("1952", "1957", "1962", "1967", "1972", "1977", "1982", "1987", "1992", "1997", "2002", "2007")
gapminder4 <- gather(gapminder3, key="year", "value"="pop", years)

head(gapminder4, n = 5)
```
#### Python
```python
from neat_panda import gather
# gapminder3 is obtained as above
gapminder4 = gather(gapminder3, key="year", value="pop", columns=range(2, 13))
# or
gapminder4 = gather(gapminder3, key="year", value="pop", columns=range(0, 2), invert_columns=True)
# or
years = ["1952", "1957", "1962", "1967", "1972", "1977", "1982", "1987", "1992", "1997", "2002", "2007"]
gapminder4 = gather(gapminder3, key="year", value="pop", columns=years)
# or
gapminder4 = gather(gapminder3, key="year", value="pop", columns=["country", "continent"], invert_columns=True)

gapminder4.head()
```
##### Output R
```
# A tibble: 5 x 4
country continent year pop
<fct> <fct> <chr> <int>
1 Afghanistan Asia 1952 8425333
2 Albania Europe 1952 1282697
3 Algeria Africa 1952 9279525
4 Angola Africa 1952 4232095
5 Argentina Americas 1952 17876956
```
##### Output Python
```
country continent year pop
0 Afghanistan Asia 1952 8425333
1 Albania Europe 1952 1282697
2 Algeria Africa 1952 9279525
3 Angola Africa 1952 4232095
4 Argentina Americas 1952 17876956

```