ks--ks

removed_by = grouped_df['removed_by'].tolist()
number_of_removed_posts = grouped_df['number_of_removed_posts'].tolist()
plt.figure(figsize=(12,8))
plt.ylabel("Number of deleted reddits")
plt.bar(removed_by, number_of_removed_posts)
plt.show()

ks--ks

q1 = """SELECT removed_by, count(distinct id)as number_of_removed_posts
FROM df 
where removed_by is not null 
group by removed_by 
order by 2 desc """

grouped_df = ps.sqldf(q1, locals())
grouped_df

ks--ks

df.info()

df.describe() #or df.count()
df.score.describe()
df.score.median()

ks--ks

df = pd.read_csv('/kaggle/input/dataisbeautiful/r_dataisbeautiful_posts.csv') 
df.head(5)

ks--ks

print(plt.style.available)

ks--ks

#plt.style.use('bmh') #setting up 'bmh' as "Bayesian Methods for Hackers" style sheet 
plt.style.use('ggplot') #R ggplot style 

ks--ks

import numpy as np #linear algebra
import pandas as pd #data processing 
import seaborn as sns #statistical graph package 
import matplotlib.pyplot as plt #plot package for visualisations 
import pandasql as ps #sql package 

ks--ks

, NTILE(100) OVER (ORDER BY recruit_score DESC, share_score DESC, days_share_score DESC, channel_share_score DESC)

ks--ks

, influence AS (
SELECT user_id 
	, recruit_score 
	, share_score 
	, days_share_score 
	, channel_share_score 
	, NTILE(100) OVER (ORDER BY recruit_score DESC, share_score DESC, days_share_score DESC, channel_share_score DESC)

FROM (
	SELECT a.user_id 

ks--ks

WITH

recruits AS (
SELECT recruiter_id AS user_id
	, item_name
	, COUNT(DISTINCT recruited_id) AS recruits
FROM recruit_table
WHERE created_at::DATE BETWEEN '2017-01-01'-180 AND '2017-01-08'
GROUP BY 1, 2
)