# -*- coding: utf-8 -*-
"""
Created on Thu Nov 18 16:14:47 2021
@author: arsii
"""
import pandas as pd
import numpy as np
import plotly.express as px
[docs]def get_timerange_(df,resample):
"""get first and last timepoint from the dataframe,
and return a resampled datetimeindex.
Parameters
----------
df : Pandas Dataframe
Dataframe containing the data
ressample : str
Resample parameter e.g., 'D' for resampling by day
Returns
-------
date_index : pd.DatatimeIndex
Resampled DatetimeIndex
"""
resample_options = ['D','H']
assert isinstance(df,pd.DataFrame), "df is not a pandas dataframe"
assert isinstance(resample,str), "resample is not a string"
assert (resample in resample_options), f"resample option: {resample} is not valid. Available options: {resample_options}."
start = df.index.min()
end = df.index.max()
if resample == 'D':
date_index = pd.date_range(start = start.strftime('%Y-%m-%d'), end = end.strftime('%Y-%m-%d'),freq='D')
if resample == 'H':
date_index = pd.date_range(start = start.strftime('%Y-%m-%d-%H'), end = end.strftime('%Y-%m-%d-%H'),freq='H')
return date_index
[docs]def combine_dataframe_(df,user_list,columns,res,date_index,agg_func=np.mean):
"""resample values from multiple users into new dataframe
Parameters
----------
df : Pandas Dataframe
Dataframe containing the data
user_list : list
List containing user names/id's (str)
columns : list
List of column names (str) to be plotted
res : str
Resample parameter e.g., 'D' for resampling by day
date_index : pd.date_range
Date range used as an index
agg_func : numpy function
Aggregation function used with resample. The default is np.mean
Returns
-------
df_comb : pd.DataFrame
Resampled and combined dataframe
"""
assert isinstance(df,pd.DataFrame), "df is not a pandas dataframe."
assert isinstance(user_list,list), "user_list is not a list."
assert isinstance(columns, list), "columns is not a list"
assert isinstance(res,str), "res is not a string."
assert isinstance(date_index,pd.core.indexes.datetimes.DatetimeIndex), "date_index is not a DatetimeIndex."
df_comb = pd.DataFrame(index=date_index)
df_comb.index = pd.to_datetime(df_comb.index)
for u in user_list:
df_temp = df[df['user'] == u][columns].resample(res).agg(agg_func)
df_temp.index = df_temp.index.strftime('%Y-%m-%d')
df_temp.index = pd.to_datetime(df_temp.index)
df_temp = df_temp.reindex(date_index)
df_comb[u] = df_temp
return df_comb
[docs]def punchcard_(df,title,n_xticks,xtitle,ytitle):
""" create a punchcard plot
Parameters
----------
df : Pandas Dataframe
Dataframe containing the data
title : str
Plot title.
n_xticks : int or None
Number of xaxis ticks. If None, scaled automatically.
xtitle : str
Plot xaxis title
ytitle : str
Plot yaxis title
Returns
-------
fig : plotly.graph_objs._figure.Figure
Punchcard plot
"""
assert isinstance(df,pd.DataFrame), "df is not a pandas dataframe."
assert isinstance(title,str), "title is not a string."
assert isinstance(n_xticks, (int,type(None))), "n_ticks is not an integer or None"
assert isinstance(xtitle,str), "xtitle is not a string."
assert isinstance(ytitle,str), "ytitle is not a string."
fig = px.imshow(df,aspect='auto',labels={'x':xtitle,'y':ytitle,'color':'Value'})
if n_xticks:
fig.update_layout(title=title,
xaxis_nticks=n_xticks,
xaxis_title=xtitle,
yaxis_title=ytitle)
else:
fig.update_layout(title=title,
xaxis_title=xtitle,
yaxis_title=ytitle)
fig.update_yaxes(tickson="labels")
fig.update_yaxes(type='category')
fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)
return fig
[docs]def punchcard_plot(df, user_list = None, columns = None, title = "Punchcard Plot", resample = 'D', normalize = False, agg_func = np.mean, timerange = False):
"""Punchcard plot for given users and column with optional resampling
Parameters
----------
df : Pandas Dataframe
Dataframe containing the data
user_list : list, optional
List containing user id's as string. The default is None.
columns : list, optional
List containing columns as strings. The default is None.
title : str, optional
Plot title. The default is "Punchcard Plot".
resample : str, optional
Indicator for resampling frequency. The default is 'D' (day).
agg_func : numpy function
Aggregation function used with resample. The default is np.mean
normalize : boolean, optional
If true, data is normalized using min-max-scaling. The default is False.
timerange : boolean or tuple, optional
If false, timerange is not filtered.
If tuple containing timestamps, timerange is filtered.
The default is False.
Returns
-------
fig : plotly.graph_objs._figure.Figure
Punchcard plot
"""
assert isinstance(df,pd.DataFrame), "df is not a pandas dataframe."
assert isinstance(user_list,(list,type(None))), "user_list is not a list or None."
assert isinstance(columns, (list,type(None))), "columns is not a list or None"
assert isinstance(title,str), "title is not a string."
assert isinstance(resample,str), "resample is not a string."
assert callable(agg_func), "agg_function is not a callable."
assert isinstance(normalize,bool), "normalize is not a boolean."
assert isinstance(timerange,(bool,tuple)), "timerange is not a boolean or tuple."
# one user
if len(user_list) == 1:
# one colums
if len(columns) == 1:
df_sel = df[df['user'] == user_list[0]][[columns[0]]].resample(resample).agg(agg_func)
if normalize:
df_sel[columns] = (df_sel[columns] - df_sel[columns].min()) / (df_sel[columns].max() - df_sel[columns].min())
fp = pd.pivot_table(df_sel, index=df_sel.index.month, values = columns[0], columns=df_sel.index.day)
fig = punchcard_(fp,title,n_xticks=31, xtitle='Day',ytitle='Month')
# multiple columns
else:
bools = df['user'].isin(user_list)
selected = []
for col in columns:
if pd.api.types.is_numeric_dtype(df[col]):
selected.append(df[bools][col].resample(resample).agg(agg_func))
else:
selected.append(df[bools][col].resample(resample).first())
df_sel = pd.concat(selected, axis=1)
if normalize:
df_sel[columns] = (df_sel[columns] - df_sel[columns].min()) / (df_sel[columns].max() - df_sel[columns].min())
fig = punchcard_(df_sel,title,n_xticks=None, xtitle='Column',ytitle='Date')
# multiple users, one column
else:
date_index = get_timerange_(df,resample)
df_comb = combine_dataframe_(df,user_list,columns,resample,date_index,agg_func)
if normalize:
df_comb =(df_comb-df_comb.min())/(df_comb.max()-df_comb.min())
if timerange:
fig = punchcard_(df_comb.loc[timerange[0]:timerange[1]].transpose(),title,n_xticks=None, xtitle='Date',ytitle='User')
else:
fig = punchcard_(df_comb.transpose(),title,n_xticks=None, xtitle='Date',ytitle='User')
return fig