AWK: single quote eche line and add comma in the end

File.csv looks like:

line1
line2
line3

Use:

cat file.csv | awk -v a="'" '{print a$0a ","}'

to make it look like:

'line1',
'line2',
'line3',

Python: Notes on Fluent Python

1.

2. List comprehension

a = [['-'] * 3 for i in range(3)]

b = [['-']*3] *3

What is the difference between a and b?

3. Inplace method

Inplace method returns None and does not create a new object. For example:

lst = [5,4,3,2,1]
lst.sort() # return None


4. Sort a list of strings by length

fruits = ['apple', 'grape', 'orange', 'banaba', 'dragon fruit']
sorted(fruits, key=len)

5. recursion

def factorial(n):
    return 1 if n<2 else="" factorial="" n-1="" n="" p="">print(factorial(5))


6. from operator import itemgetter, attrgetter, methodcaller

Pandas: groupby and find the most frequent item

Say I have this dataframe:

order_id | class
 1 |  furniture
2  |  book
2  | furniture
2  | book
3  | auto
3  | auto
3  | electronics
3  | pet

and to get the most frequent class of each order:

df.groupby('order_id').agg({'order_id': lambda x: x.value_counts().index[0]})

Make a simple heatmap in R with ggplot2

So today I got a file that look like this:

And here's the end result of the heat map:

(Notice that the order of the Name in the chart is not the same as that in the dataframe.)

Here's the code I used to make this plot:

rm(list=ls())
library(ggplot2)

df = read.csv('fakedata.csv')

# reshape the dataframe
df.m = melt(df, id.vars = 'Name')


ggplot(df.m, aes(variable, Name)) +
  geom_tile(aes(fill = value),
            colour = "white") +
  scale_fill_gradient(low = 'white',
                      high = 'blue4')



This is how the df.m look like:

convert a sqlite3 query result table into a pandas dataframe

The sqlite3 query returns a list of tuples. Here's a good way to convert the list of tuples into a pandas data frame.

def q():
    sql_c = " some query commands here"
    df = pd.DataFrame(dbc.execute(sql_c).fetchall())
   
    # get all column names
    cns = [d[0] for d in dbc.description]
    df.columns = cns

    return df

DataFrame create multiple new columns by applying a function that returns multiple

df[['sum', 'difference']] = df.apply(
    lambda row: add_subtract_list(row['a'], row['b']), axis=1)

vundle for vim plugin, use this one!

https://github.com/vim-scripts/vundle