Playground jupyter

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from bokeh.plotting import figure, output_notebook, show
from bokeh.layouts import column
output_notebook()

# read csf file
df = pd.read_csv('data/actstream.csv', index_col='timestamp', parse_dates=True)

# 특정 열의 값 필터링하기
df = df[(df.actor != 'konolabs') & (df.actor != 'konobeta') & (df.actor != 'test_yun') & (df.actor != 'NBA') 
        & (df.target != 'konobeta') & (df.target != 'konolabs') 
        & (df.verb != 'message') & (df.verb != 'member_joined_channel')]

# 특정 행을 그룹하고 특정행을 기준으로 내림차순 정렬
# - actor로 그룹
# - description을 기준으로 내림차순 정렬
res = df.groupby('actor').count().sort_values(['description'], ascending=False).head(20)

# matplotlib 으로 그림 출력
x = df.groupby(df.index.date).count().index
y = df.groupby(df.index.date).count()['description']
plt.figure(figsize=(15,5))
plt.plot(x, y)

from bokeh.models import ColumnDataSource, HoverTool
# create a new plot with a title and axis labels

p = figure(title="Daily usages of kono bot",
           x_axis_label='날짜', y_axis_label='개수', x_axis_type="datetime",
           plot_width=800, plot_height=350)

p.add_tools(HoverTool(
    tooltips=[
    ( 'date',   '@{x}{%F}'            ),
    ("total", "@y"),
    ],

    formatters={
        'x'      : 'datetime', # use 'datetime' formatter for 'date' field
    },
))

# add a line renderer with legend and line thickness
p.vbar(x, top=y, legend="Number of messages", width=100, color='blue')

# show the results
show(p)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from bokeh.plotting import figure, output_notebook, show
from bokeh.layouts import column
output_notebook()

✗ pip install notebook pandas matplotlib bokeh