Introduction to the Essex Senior Cup
The Essex Senior Cup stands as one of England's most prestigious football tournaments, attracting teams from across the region. Known for its rich history and competitive spirit, the cup draws in both seasoned and emerging football clubs. This article provides an in-depth look at the tournament, offering expert betting predictions and insights into the latest matches.
Historical Significance
The Essex Senior Cup has been a cornerstone of English football since its inception. It serves not only as a platform for local talent but also as a stepping stone for players aiming to reach higher leagues. The cup's history is filled with memorable matches and legendary performances that have left an indelible mark on the sport.
Current Season Overview
Each season of the Essex Senior Cup brings fresh excitement with new teams vying for glory. The current season promises thrilling encounters, with clubs showcasing their best talents. Fans can expect high-octane matches filled with strategic plays and unexpected twists.
Match Schedule and Updates
The match schedule is updated daily, ensuring fans have access to the latest information. This dynamic schedule allows for real-time tracking of team progress and standings, keeping enthusiasts engaged throughout the tournament.
Key Matches to Watch
- Team A vs Team B: A classic rivalry that never fails to deliver excitement.
- Team C vs Team D: Known for their aggressive play style, this match promises intense competition.
- Team E vs Team F: A clash between two rising stars of the league.
Betting Predictions
Betting on football adds an extra layer of thrill to watching matches. Expert predictions provide insights into potential outcomes based on team form, player statistics, and historical performance.
Prediction Methodology
- Analyzing past performance data of teams involved.
- Evaluating player fitness and recent form.
- Considering head-to-head statistics between competing teams.
- Incorporating expert opinions from seasoned analysts.
Detailed Predictions for Upcoming Matches
Team A vs Team B
This match is anticipated to be a close contest. Team A has shown strong defensive capabilities recently, while Team B boasts an impressive attacking lineup. Experts predict a draw, but with a slight edge towards Team B due to their recent home victories.
Fan Engagement and Community
The Essex Senior Cup fosters a strong sense of community among fans. Social media platforms buzz with discussions about upcoming matches, player performances, and betting odds. Engaging with fellow fans enhances the overall experience of following the tournament.
Social Media Tips
- Follow official team pages for real-time updates and behind-the-scenes content.
- Join fan groups on platforms like Facebook or Twitter to share thoughts and predictions with others.
<|diff_marker|> ---assistant- Create fan polls or prediction contests to engage your network during matches.
<|diff_marker|> ADD A1000
<|diff_marker|> ***
<|diff_marker|> REMOVE AT START OF FILE
*** Excerpt data for ID: **1 ***
*** Conversation ***
## Suggestions for complexity
1. **Tournament Structure Analysis:** "How does altering specific rules in knockout stages affect overall team strategies? For example, if extra time was replaced by instant replay decisions instead?"
2. **Historical Contextualization:** "Can you explain how changes in refereeing technology over decades have influenced game outcomes in tournaments like these?"
3. **Statistical Impact:** "What are some advanced statistical methods used to predict outcomes in knockout stages? How reliable are they compared to traditional analysis?"
4. **Cultural Influence:** "How do cultural differences among teams influence tactical approaches in knockout stages? Can you provide examples from different eras?"
5. **Hypothetical Scenario:** "If we applied modern-day training techniques retroactively to past tournaments' teams during knockout stages, how might results differ?"
## Conversation
<|user|>"In knockout stage competitions like those mentioned above, what are some less obvious factors that can significantly alter a team's performance?"<|end|>
<|assistant|>"There are several nuanced factors that can impact performance in knockout stages beyond just skill level or physical fitness:
1. **Psychological Pressure:** The mental strain of knowing there's no room for error can lead to uncharacteristic mistakes.
2. **Travel Fatigue:** Teams traveling long distances might suffer from jet lag or fatigue.
3. **Referee Decisions:** Subtle biases or inconsistencies from referees can tilt games.
4. **Home Advantage Dynamics:** Playing at home might boost morale but also increase pressure due to high expectations.
5. **Weather Conditions:** Unexpected weather changes can disrupt planned tactics.
These elements often interplay in complex ways."
<|user|>"Interesting points! How would altering referee decision-making processes—like replacing extra time decisions with instant replays—affect team strategies?"<|end|>
<|assistant|>"Replacing extra time decisions with instant replay could fundamentally shift strategies:
1. **Risk Management:** Teams might take fewer risks near critical moments knowing decisions will be reviewed closely.
2. **Time Management:** Coaches could focus more on controlling game tempo rather than rushing plays towards end times.
3. **Player Fatigue Considerations:** Knowing that crucial moments will be reviewed might reduce fatigue management concerns slightly since players wouldn't need frantic efforts at every last second.
This change could lead coaches to emphasize precision over aggression during high-stakes periods."
<|user|>"How has refereeing technology evolved over decades impacted game outcomes specifically in knockout stages?"<|end|>
<|assistant|>"The evolution of refereeing technology has had profound impacts:
1. **Goal-Line Technology (GLT):** Introduced primarily after controversial incidents where goals were either wrongly awarded or denied; it reduces such errors dramatically.
2. **Video Assistant Referee (VAR):** Provides retrospective analysis which helps correct major errors but also introduces delays that can disrupt game flow.
3. **Communication Devices:** Better communication tools allow referees more immediate access to support staff during contentious moments.
These advancements generally lead to fairer outcomes but also add layers of complexity regarding when interventions should occur."
<|user|>"Can you elaborate on advanced statistical methods used today compared to traditional analysis when predicting outcomes in knockout stages?"<|end|>
<|assistant|>"Certainly! Advanced statistical methods include:
1. **Machine Learning Models:** These models analyze vast datasets including player stats, historical performances under similar conditions etc., providing predictive analytics far beyond human capability.
2. **Bayesian Inference:** Uses prior knowledge combined with current data trends to update probabilities dynamically as games progress.
3. **Network Analysis:** Examines relationships between players’ interactions on field—how passing patterns evolve under pressure scenarios typical in knockouts.
Traditional methods relied heavily on subjective expert opinions or simpler metrics like win-loss records without considering deeper contextual nuances."
<|user|>"Fascinating! What role do cultural differences among teams play in shaping tactical approaches during these stages?"<|end|>
<|assistant|"Cultural differences significantly influence tactics:
1. ****Attacking Styles**: Some cultures emphasize technical skill (e.g., Brazilian flair) while others prioritize physicality (e.g., Northern European robustness).
2 ****Defensive Philosophy**: Cultures valuing discipline may focus more on structured defense (e.g., Italian catenaccio), whereas others may adopt more fluid systems.
Examples:
- In earlier eras, Brazilian teams were known for their expressive attacking play which sometimes clashed against rigid defenses like those seen in German squads.
- More recently, Asian teams have started blending Western tactical discipline with unique regional styles.
Such variations often dictate how adaptable or rigid teams are under high-pressure situations typical in knockouts."
[0]: import numpy as np
[1]: import matplotlib.pyplot as plt
[2]: import pandas as pd
[3]: import os
[4]: import seaborn as sns
[5]: def plot_bars(data,
[6]: labels=None,
[7]: title=None,
[8]: xlabel=None,
[9]: ylabel=None,
[10]: xticks_rotation=0,
[11]: figsize=(8,6),
[12]: legend_loc='best',
[13]: legend_cols=1,
[14]: legend_fontsize='medium',
[15]: grid=False,
[16]: save_fig=False):
def get_data(path):
# Importing dataset
df = pd.read_csv(path)
# Selecting features
features = ['country', 'year', 'sex', 'age', 'suicides_no', 'population']
# Selecting features
df = df.loc[:, features]
return df
def get_country_data(df):
# Grouping by country
country_df = df.groupby('country')
# Getting count per country
country_count = country_df['suicides_no'].count().reset_index(name='count')
return country_count.sort_values(by=['count'], ascending=False)
def get_year_data(df):
# Grouping by year
year_df = df.groupby('year')
# Getting sum per year
year_sum = year_df['suicides_no'].sum().reset_index(name='sum')
return year_sum.sort_values(by=['year'])
def get_age_data(df):
# Grouping by age
age_df = df.groupby('age')
# Getting mean per age group
age_mean = age_df['suicides_no'].mean().reset_index(name='mean')
return age_mean.sort_values(by=['mean'], ascending=False)
def get_sex_data(df):
# Grouping by sex
sex_df = df.groupby('sex')
# Getting sum per sex
sex_sum = sex_df['suicides_no'].sum().reset_index(name='sum')
return sex_sum.sort_values(by=['sum'], ascending=False)
def get_age_sex_data(df):
data_dict = {}
data_dict['Male'] = []
data_dict['Female'] = []
data_dict['age'] = []
for i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d]in zip(df.sex.unique(),df.age.unique(),df.suicides_no.unique(),df.population.unique(),df.year.unique()):
if j not in data_dict['age']:
data_dict['age'].append(j)
data_dict['Male'].append(i)
data_dict['Female'].append(i)
else:
pass
for i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d]in zip(data_dict.get('Male'),data_dict.get('Female'),data_dict.get('age'),df.suicides_no.unique(),df.population.unique()):
if j == k:
data_dict.get(k).remove(j)
else:
pass
df_new=pd.DataFrame.from_dict(data_dict)
return df_new.set_index(['age']).sort_index()
def get_top_countries_yearly_suicide_rates(df):
suicide_rate_per_year_by_country=df.copy()
suicide_rate_per_year_by_country["suicide_rate"]=suicide_rate_per_year_by_country["suicides_no"]/suicide_rate_per_year_by_country["population"]*100000
top_20_countries=suicide_rate_per_year_by_country.groupby("country").agg({"country":"count"}).sort_values("country",ascending=False).head(20).index.values
top_20_countries_suicide_rates=suicide_rate_per_year_by_country[suicide_rate_per_year_by_country["country"].isin(top_20_countries)]
top_20_countries_suicide_rates=top_20_countries_suicide_rates.groupby(["country","year"]).agg({"suicide_rate":"mean"}).unstack("year")
top_20_countries_suicide_rates.columns=top_20_countries_suicide_rates.columns.droplevel()
top_20_countries_suicide_rates=top_20_countries_suicide_rates.reset_index()
top_20_countries_suicide_rates=top_20_countries_suicide_rates.melt(id_vars=["country"])
top_20_countries_suicide_rates.columns=["Country","Year","Suicides/100k pop"]
top_20_countries_suicide_rates.Year=top_20_countries_suicide_rates.Year.astype(int)
return top_20_countries_suicide_rates
def plot_top_countriestopics(top_topics,top_topics_names,num_topics_to_show=10):
dict_top_topics={}
dict_top_topics_names={}
for topic_id,(topic_words,name)in enumerate(zip(top_topics,top_topics_names)):
dict_top_topics[str(topic_id)]=(topic_words[:num_topics_to_show])
dict_top_topics_names[str(topic_id)]=name
df=pd.DataFrame(dict_top_topics)
df=df.T.reset_index()
df=df.rename(columns={"index":"Topic Id"})
df=df.melt(id_vars=["Topic Id"],value_name="Top Words")
df["Topic Name"]=df["Topic Id"].map(dict_top_topics_names)
plt.figure(figsize=(15,len(top_topics)*0+5))
ax=sns.barplot(x="Topic Name",y="Top Words",data=df,color="#69b3a2")
ax.set_xticklabels(ax.get_xticklabels(),rotation=40,size=10)
plt.title("Top Words Per Topic")
plt.xlabel("Topics")
plt.ylabel("Words")
plt.show()
def plot_sns_barplot(data,label_col,title='',xlabel='',ylabel='',xticks_rotation=0,size=(8,6),legend_loc='best',legend_cols=1,axis_off=True):
sns.set(style="whitegrid")
f=plt.figure(figsize=size)
ax=f.add_subplot(111)
if axis_off==True:
ax.spines["top"].set_visible(False) # Remove Plot Borders
ax.spines["right"].set_visible(False) # Remove Plot Borders
ax.spines["bottom"].set_visible(False) # Remove Plot Borders
ax.spines["left"].set_visible(False) # Remove Plot Borders
f.patch.set_facecolor('#f5f5f5') # Set Figure Background Color
else:
pass
bars=sns.barplot(x=label_col,data=data)# Plotting Bar Chart Using Seaborn Library
bars.set_title(title)# Setting Title Of Chart
bars.set_xlabel(xlabel)# Setting X Axis Label Of Chart
bars.set_ylabel(ylabel)# Setting Y Axis Label Of Chart
bars.tick_params(labelsize=12)# Setting Size Of Ticks On X And Y Axis Of Chart
bars.set_xticklabels(bars.get_xticklabels(),rotation=xticks_rotation)# Rotating Ticks On X Axis By Specified Degrees
bars.legend(loc=legend_loc,numpoints=1,columnspacing=.75,columnwidth=.75,labelspacing=.25,borderpad=.25,borderaxespad=.75,# Setting Legend Location And Styling Legend Box And Labels
fontsize=legend_fontsize,# Setting Font Size For Legend Labels
ncol=legend_cols,# Setting Number Of Columns In Legend Box
fancybox=True,# Using Fancy Box For Legend Box
shadow=True,# Adding Shadow To Legend Box
frameon=True)# Keeping Frame On Legend Box Or Not
if save_fig==True:# Saving Figure If Condition Is True
f.savefig(title+".png")# Saving Figure With Specified File Name Format .png Extension And Specified Title As File Name.
else:
pass
plt.show()
path=r'D:Data ScienceProjectsSuicidality Data Explorationmaster.csv'
df=get_data(path)
country_count=get_country_data(df)
top_ten_countrys=country_count.head(10)
top_ten_countrys_labels=list(top_ten_countrys.country.values)
top_ten_countrys_counts=list(top_ten_countrys.count.values)
plot_bars(
data=top_ten_countrys_counts,
labels=top_ten_countrys_labels,
title='Top Ten Countries With Most Suicidal Cases',
xlabel='Countries',
ylabel='# Suicidal Cases',
xticks_rotation=45,
figsize=(8*1+6*0+6*0+6*0+6*0+6*0+6*0+6*0+6*0+6*0)/10*(8)+((8*1+6*0+6*0+6*0+6*0+6*0+6*0+6*0+6*0)/9*(8)+((8)*9/9*(8)+((8)*9/9*(8)))*9/10),
legend_loc='upper right',
legend_cols=1,
legend_fontsize='medium',
grid=True,
save_fig=False)
years=get_year_data(df)
years_labels=list(years.year.values)
years_cases=list(years.sum.values)
plot_bars(
data=years_cases,
labels=years_labels,
title='Suicidal Cases Per Year',
xlabel='Years',
ylabel='# Suicidal Cases',
xticks_rotation=-45,
figsize=(8+(len(years_labels)-7)*(len(years_labels)-7)/14*(8))/len(years_labels)*(8)+(len(years_labels)-7)*(len(years_labels)-7)/14*(8),
legend_loc='upper right',
legend_cols=len(years_labels)//15 + len(years_labels)%15//len(years_labels)%15 + len(years_labels)%15%len(years_labels)%15//len(years_labels)%15%len(years_labels)%15 + len(years_labels)%15%len(years_labels)%15%len(years_labels)%15//len(years_labels)%15%len(years_labels)%15%len(years_labels)%15 + len(years_labels)%15%len(years_labels)%15%len(years_labels)%15%len years(labels)//length years(labels),
legend_fontsize='medium',
grid=True,
save_fig=False)
ages=get_age_data(df)
ages_groups=list(ages.age.values)
ages_mean_cases=list(ages.mean.values)
plot_bars(
data=[ages_mean_cases],
labels=[ages_groups],
title=['Age Groups Vs Mean Suicidal Cases'],
xlabel=['Age Groups'],
ylabel=['Mean Suicidal Cases'],
xticks_rotation=[90],
figsize=((max([np.mean([max(i)for iin ddatan])for datanamesdtaindata])+(min([np.mean([min(i)for iin ddatan])for datanamesdtaindata]))/4*(max([np.mean([max(i)for iin ddatan])for datanamesdtaindata]))+(min([np.mean([min(i)for iin ddatan])for datanamesdtaindata])))*tuple(len(data)+tuple(len(labels))+tuple(len(title))+tuple(len(xlabel))+tuple(len(ylabel))+tuple(len(xticks_rotation)))/sum(map(len,[data]+list(labels)+list(title)+list(xlabel)+list(ylabel)+list(xticks_rotation))),
legend_loc=['lower right'],
legend_cols=[int(sum(list(map(len,[data]+list(labels)+list(title)+list(xlabel)+list(ylabel)+list(xticks_rotation))))/11 )],
legend_fontsize=['medium'],
grid=[True],
save_fig=[False])
genders=get_sex_data(df)
genders_list=list(genders.sex.values)
genders_sum_cases=list(genders.sum.values)
plot_bars(
data=[genders_sum_cases],
labels=[genders_list],
title=['Genders Vs Sum Suicidal Cases'],
xlabel=['Genders'],
ylabel=['Sum Suicidal Cases'],
xticks_rotation=[90],
figsize=((max([np.mean([max(i)for iin ddatan])for datanamesdtaindata])+(min([np.mean([min(i)for iin ddatan])for datanamesdtaindata]))/4*(max([np.mean([max(i)for iin ddatan])for datanamesdtaindata]))+(min([np.mean([min(i)for iin ddatan])for datanamesdtaindata])))*tuple(len(data)+tuple(len(labels))+tuple(len(title))+tuple(len(xlabel))+tuple(len(ylabel))+tuple(len(xticks_rotation)))/sum(map(len,[data]+list(labels)+list(title)+list(xlabel)+list(ylabel)+list(xticks_rotation))),
legend_loc=['lower right'],
legend_cols=[int(sum(list(map(len,[data]+list(labels)+list(title)+list(xlabel)+list(ylabel)+list(xticks_rotation))))/11 )],
legend_fontsize=['medium'],
grid=[True],
save_fig=[False])
gender_age=get_age_sex_data(df)
male_list=list(gender_age.Male.dropna())
female_list=list(gender_age.Female.dropna())
gender_age_groups=list(gender_age.age.dropna())
plot_bars(
data=[[male_list],[female_list]],
labels=[[gender_age_groups],[gender_age_groups]],
title=['Male Age Groups Vs Mean Suicidal Cases','Female Age Groups Vs Mean Suicidal Cases'],
xlabel=['Age Groups','Age Groups'],
ylabel=['Mean Suicidal Cases','Mean Suicidal Cases'],
xticks_rotation=[90,-90],
figsize=((max([(i+j+k+l+m+n)[i+j+k+l+m+n].index(max([(i+j+k+l+m+n)[i+j+k+l+m+n]))/(j+k+l+m+n)[j+k+l+m+n].index(min([(j+k+l+m+n)[j+k+l+m+n]))/(k+l+m+n)[k+l+m+n].index(max([(k+l+m+n)[k+l+m+n]))/(l+m+n)[l+m+n].index(min([(l+m+n)[l+m+n]))/(m+n)[m+n].index(max([(m+n)[m+n]))/(n)[n].index(min([(n)))][i:j:k:l:m:n])/((j+k+l+m+n)[j+k+l+m+n].index(min([(j+k+l+m+n))[j+k+l+m+n]/((k+l+m+n)[k+l+m+n].index(max([(k+l+m+n))[k+l+m+])))]/((l+[m+[n)])][l:m:n])/((m+[n])[m:[n]).index(min([(m+[n))[m:[n)]))/((n])[n].index(max([(n))[n)])])*tuple(sum(map(lambda x:len(list(filter(lambda y:y!='None'and y!=None,list(getattr(plot_bars,'__dict__').get(str('_').join(str.split(str(repr({x})).split("'")))).get('__self__').get('__dict__').get('_'+str('_'.join(str.split(str(repr({x})).split("'"))))).get('__args__')))))))),
legend_loc[['upper left'],['upper right']],
legend_cols[[int(sum(list(map(lambda x:len(list(filter(lambda y:y!='None'and y!=None,list(getattr(plot_bars,'__dict__').get(str('_').join(str.split(str(repr({x})).split("'")))).get('__self__').get('__dict__').get('_'+str('_'.join(str.split(str(repr({x})).split("'"))))).get('__args__')))))))/(11))],[int(sum(list(map(lambda x:len(list(filter(lambda y:y!='None'and y!=None,list(getattr(plot_bars,'__dict__').get(str('_'.join(str.split(str(repr({x})).split("'")))).get('__self__').get('__dict__').get('_'+str('_'.join(str.split(str(repr({x})).split("'"))))).get('__args__')))))))/(11)))] ,
legend_fontsize[['medium'],['medium']],
grid=[[True],[True]],
save_fig=[[False],[False]])
top_twenty_yearsly_suiide_ratess=get_top_countries_yearly_suide_ratess(suicides_number_dataframe)
top_twenty_yearsly_suiide_ratess_plot=top_twenty_yearsly_suiide_ratess.pivot(index="Year",columns="Country",values="Suicides/100k pop")
sns_plot=sns.lineplot(data=top_twenty_yearsly_suiide_ratess_plot)
sns_plot.set_title("Top Twenty Countries Suicide Rates Per Year")
sns_plot.set_xlabel("Year")
sns_plot.set_ylabel("Suicides Per Hundred Thousand People")
***** Tag Data *****
ID: 5
description: Function `plot_bars` uses nested list comprehensions within `figsize`
calculation based on input lengths using advanced indexing techniques.
start line: 41
end line: 98
dependencies:
- type: Function
name: plot_bars
start line: 41
end line: 98
context description: The function plots bar charts using seaborn library but contains
highly complex logic within `figsize` calculation involving multiple nested comprehensions,
algorithmic depth: 4
algorithmic depth external: N
obscurity: 5
advanced coding concepts: 5
interesting for students: 5
self contained: N
************
## Challenging aspects
### Challenging aspects in above code
The provided code snippet involves several challenging aspects that require careful consideration:
1. **Complex `figsize` Calculation**: The `figsize` parameter uses deeply nested list comprehensions involving multiple layers of calculations based on various input parameters (`data`, `labels`, `title`, etc.). Understanding how each component contributes requires thorough comprehension of Python’s list comprehension mechanics and mathematical operations within them.
2. **Dynamic Input Handling**: The function must handle varying lengths and types of inputs (`data`, `labels`, etc.) gracefully while maintaining consistency across different parameters (e.g., ensuring all lists are properly aligned).
3. **Seaborn Integration**: Integrating Seaborn’s plotting capabilities seamlessly while customizing various aesthetic properties (e.g., removing spines from plots).
4. **Conditional Logic**: Implementing conditional logic correctly so that certain actions (like saving figures or adjusting figure properties based on user input flags such as `axis_off`).
### Extension
To extend these challenges further:
1. Add functionality allowing users to pass additional customizations such as colors per bar category or custom annotations directly onto bars.
2 Introduce handling scenarios where input data could be dynamically changing during execution (e.g., streaming data).
3 Allow customization options through additional parameters such as custom tick formats or secondary axes plotting capabilities.
## Exercise
### Task Description
You will expand upon [SNIPPET] by introducing additional functionalities while maintaining existing logic complexities:
#### Requirements:
1 Extend `plot_bars` function:
* Introduce an additional parameter `colors` which accepts a list specifying colors per bar category.
* Add functionality allowing users to pass annotations directly onto bars via a new parameter called `annotations`.
* Ensure compatibility between new parameters (`colors`, `annotations`) with existing ones (`save_fig`, etc.).
#### Constraints:
* Colors should default uniformly if not specified by users.
* Annotations should align perfectly atop corresponding bars even when rotated ticks exist.
#### Additional Challenge:
Enhance error handling mechanisms within your implementation ensuring robustness against improper inputs such as mismatched lengths between primary parameters (`data`, `labels`) versus newly introduced ones (`colors`, `annotations`).
### Full exercise here:
Expand [SNIPPET] according to requirements above.
python
import matplotlib.pyplot as plt
import seaborn as sns
def plot_bars(
data,
labels,
title='',
xlabel='',
ylabel='',
xticks_rotation=0,
figsize=(8,6),
legend_loc='best',
legend_cols=1,
legend_fontsize='medium',
grid=False,
save_fig=False,
axis_off=True,
colors=None,
annotations=None):
sns.set(style="whitegrid")
f=plt.figure(figsize=size)
ax=f.add_subplot(111)
if axis_off==True:
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["left"].set_visible(False)
f.patch.set_facecolor('#f5f5f5')
bars=sns.barplot(
x=data,
y=data.index,
palette=colors if colors else None)
if annotations:
for bar_container , annotation_text in zip(bars.containers , annotations):
bar_container_height=barm.patches.height()
annotation_x_pos=barm.patches.xpos()
annotation_y_pos=barm.patches.ypos()+bar_container_height /20000
ax.text(annotation_x_pos , annotation_y_pos , annotation_text)
bars.set_title(title)
bars.set_xlabel(xlabel)
bars.set_ylabel(ylabel)
bars.tick_params(labelsize=12)
bars.set_xticklabels(bars.get_xticklabels(), rotation=x_ticks_rotatation)
bars.legend(loc=lengend_locaiton , numpoints=lengend_num_points , columnspacing=lengend_column_spacing ,
columnwidth=lengend_column_width , labelspacing=lengend_label_spacing ,
borderpad=lengend_border_pad , borderaxespad=lengend_border_axes_pad ,
fontsize=lengend_fonze_size , ncol=lengend_num_columns ,
fancybox=true , shadow=true , frameon=true )
if __name__ == "__main__":
## Solution
python
import matplotlib.pyplot as plt
import seaborn as sns
def plot_bares(
data,
labels,
title='',
xlabel='',
ylabel='',
xtick_rotatation_degreees=int(),
figsize=(18,int(figuresize)), ## calculated dynamically based on length & nature fo inputs ##
legent_location=str(),
legent_columns=int(),
legent_fomnt_size=str(),
show_grid=bool(),
save_figure=bool(),
hide_axis=bool(),
color_palette=[] ,
annotate=[]):
sns.plot_style=("whitegrid")
figure_object=plt.figurize(size_tuple)
sub_plot_figure_object=
figure_object.subfigure(subplots_adjustment)
if hide_axis==true:
sub_plot_figure_object.remove_spine(axis_direction=["toptop","right","bottom","left"])
figure_object.background_color("#f55f55")
bares_plot=
seaborn.bareplots(
orientation_axis=["horizontal"],
value_axis=data ,
categorical_axis=data.index ,
color_palette=color_palette)
if annotate :
bar_containers=[]
annotation_texts=[]
length_of_bar_containers=len(bar_containers)
length_of_annotation_texts=len(annotation_texts)
if length_of_bar_containers != length_of_annotation_texts :
raise ValueError ("Length mismatch between annotations & bar containers.")
else :
height_of_each_bar_container=
[container.height() for container_in containers]
position_x_for_each_annotation=
[container.xpos() fro container_in containers]
position_y_for_each_annotation=
[(height_of_each_bar_container[i]/20000)+
position_x_for_each_annotation[i]
foreach index_i_in range(length_of_bar_containers)]
[ax.text(position_x_for_each_annotation[i],
position_y_for_each_annotation[i],
annotation_texts[i])
foreach index_i_in range(length_of_bar_containers)]
bares_plots.title=title
bares_plots.xlabel=x_label
bares_plots.ylabel=y_label
bares_plots.ticked_params(lables_size=int())
bares_plots.xticks(rotation=x_tick_rotatation_degreees)
bares_plots.lengends(
location_legent_location[
number_points_in_legend=int() ,
spacing_between_columns=float(),
width_of_columns=float(),
spacing_between_lablels=float(),
padding_between_legent_border=float(),
padding_between_legent_axes=float(),
font_size_legents=str(),
number_columns_in_legents=int(),
box_style_legents=bool(),
shadow_on_legents=bool(),
border_on_legents=bool()])
if save_figure==true :
figure_object.save_image_as_png_file(filename=title+".png")
## Follow-up exercise
### Follow-up Exercise Description
Enhance your solution further by adding functionalities which allow real-time updating of plots when new data arrives dynamically without re-rendering entire graph again!
#### Requirements:
Add support so that when new datasets arrive they update existing plots dynamically instead re-drawing whole chart anew!
#### Constraints:
Ensure minimal flickering & efficient resource usage even when continuously updating graphs!
#### Solution Outline:
Utilize Matplotlib’s animation framework alongside Seaborn’s dynamic plotting capabilities making sure efficient memory management & smooth visual transitions!
python
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
class DynamicBarPlotter:
def __init__(self):
self.fig,self.ax=plt.subplots()
self.barchart_handle=None
def update(self,new_datapoints,new_lables):
if self.barchart_handle is None:
self.barchart_handle=self.ax.bar(new_lables,new_datapoints,color="#FF5733")
else :
heights=np.array(self.barchart_handle.get_height())
heights+=new_datapoints
self.barchart_handle.__init__(self.ax,height=new_height,width=self.barchart_handle.width,color="#FF57333")
def animate(self,data_generator_function,interval_time_ms:int):
anim_func=lambda frame:self.update(*next(data_generator_function))
return FuncAnimation(self.fig,self.anim_func,interval_time_ms)
if __name=="main":
def random_generator():
while True :
yield(np.random.randint(low_range,max_range),range(count))
dynamic_plooter_objct=DyanmicBarPlotter()
animation_objct=dynamic_plooter_objct.animate(random_generator,interval_time_ms:interval_time_milliseconds)
plt.show()
*** Excerpt ***
A substantial body of research has suggested that low socioeconomic status (SES; defined here broadly by income level relative both within one’s own generation – individual SES – and across generations – family SES), is associated with worse health outcomes [23–26], although causal pathways remain unclear [27–29]. Low SES is associated not only with lower life