Note: this is a fork of @Conormacd converted to markdown from raw text. All content is Conor's.

R to python useful data wrangling snippets

The dplyr package in R makes data wrangling significantly easier.
The beauty of dplyr is that, by design, the options available are limited.
Specifically, a set of key verbs form the core of the package.
Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe.
Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R.
The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs

`filter`: subset a dataframe according to condition(s) in a variable(s)
`select`: choose a specific variable or set of variables
`arrange`: order dataframe by index or variable
`group_by`: create a grouped dataframe
`summarise`: reduce variable to summary variable (e.g. `mean`)
`mutate`: transform dataframe by adding new variables

The excellent pandas package in Python easily allows you to implement all of these actions (and much, much more!). Below are some snippets to highlight some of the more basic conversions.

I'll update this on a regular basis with more complex snippets.
Thanks!
Conor @Conormacd

# Function Equivalents

## Filter
### R
```
filter(df, var > 20000 & var < 30000)
filter(df, var == 'string') # df %>% filter(var != 'string')
df %>% filter(var != 'string')
df %>% group_by(group) %>% filter(sum(var) > 2000000)
```

### Python
```
df[(df['var'] > 20000) & (df['var'] < 30000)]
df[df['var'] == 'string']
df[df['var'] != 'string']
df.groupby('group').filter(lambda x: sum(x['var']) > 2000000)
```
## Select
### R
```
select(df, var1, var2)
select(df, -var3)
```

### Python
```
df[['var1', 'var2']]
df.drop('var3', 1)
```

## Arrange
### R
```
arrange(df, var1)
arrange(df, desc(var1))
```

### Python
```
df.sort_values('var1')
df.sort_values('var1', ascending=False)
```

## Grouping
### R
```
df %>% group_by(group)
df %>% group_by(group1, group2)
df %>% ungroup()
```

### Python
```
df.groupby('group1')
df.groupby(['group1', 'group2'])
df.reset_index() / or when grouping: df.groupby('group1', as_index=False)
```

## Summarise / Aggregate df by group
### R
```
df %>% group_by(group) %>% summarise(mean_var1 = mean(var1))
df %>% group_by(group1, group2) %>% summarise(mean_var1 = mean(var1),
                                              sum_var1 = sum(var1),
                                              count_var1 = n())
```

### Python
```
df.groupby('group1')['var1'].agg({'mean_col' : np.mean()}) # pass dict to specifiy column name

df.groupby(['group1', 'group2'])['var1]'].agg(['mean', 'sum', 'count']) # for count also consider 'size'. size will return n for NaN values also, whereas 'count' will not.
```

## Mutate / transform df by group
### R
```
df %>% group_by(group) %>% mutate(mean_var1 = mean(var1))
```

### Python
```
df.groupby('group')['var1'].transform(np.mean)
```

## Distinct
remove duplicate obs from data frame
### R
```
df %>% distinct()
df %>% distinct(col1) # returns dataframe with unique values of col1
```

### Python
```
df.drop_duplicates()
df.drop_duplicates(subset='col1') # returns dataframe with unique values of col1
```

## Sample
generate random samples of the data by n or by %
### R
```
sample_n(df, 100)
sample_frac(df, 0.5)
```

### Python
```
df.sample(100)
df.sample(frac=0.5)
````