I am getting glitchy graphs can anyone help?

Dash Python
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Can someone recommend how I can fix my glitchy graphs?

2

Hello @angusm112,

Welcome to the community!

It looks like something about your data isn’t quite right.

Have you made sure to sum the data before representing it?

Also, code helps go a long way. If you could please provide the example app for how this is demonstrating, then that will help us, help you. :grin:

3

Hi @angusm112 !

My guess is that you used a bar plot, you should try with a histogram, take a look here:

Or as suggested by @jinnyzor you should aggregate your data before render it with your bar plot.
That would be easier indeed with some code :grin:

4

Hi thank you for your help on this - yes I can show you the code:

its a matplotlib graph:

#PLOT 1

Sort the DataFrame by Crime Rate in descending order to highlight the highest crime rate state

usacrime_df_sorted = usacrime_df.sort_values(by=‘Crime Rate’, ascending=False)

Select the state with the highest crime rate

highest_crime_state = usacrime_df_sorted.iloc[0]

Plotting

plt.figure(figsize=(12, 8))

Plot bars for all states in grey

plt.bar(usacrime_df_sorted[‘States’], usacrime_df_sorted[‘Crime Rate’], color=‘grey’)

Highlight the highest crime rate state (Florida) in red

plt.bar(highest_crime_state[‘States’], highest_crime_state[‘Crime Rate’], color=‘red’, label=‘Florida’)

Annotate Florida’s crime rate on the graph

plt.text(highest_crime_state[‘States’], highest_crime_state[‘Crime Rate’], highest_crime_state[‘Crime Rate’],
ha=‘center’, va=‘bottom’)

plt.title(‘States with Crime Rates (1975-2015)’)
plt.xlabel(‘States’)
plt.ylabel(‘Crime Rate Count’)
plt.xticks(rotation=45, ha=‘right’)
plt.tight_layout() # Adjust layout to prevent clipping of labels
plt.legend() # Show legend
plt.show()

#Creating a new colomn called ‘Crime Rate Average’ to look at Average crime rates amoungst all States

Check the data types of each column

print(usacrime_df.dtypes)

If any of the columns are not numeric, you can convert them to numeric data type

usacrime_df[‘Crime Rate’] = pd.to_numeric(usacrime_df[‘Crime Rate’], errors=‘coerce’)
usacrime_df[‘Homicide Rate’] = pd.to_numeric(usacrime_df[‘Homicide Rate’], errors=‘coerce’)
usacrime_df[‘Rape Crime Rate’] = pd.to_numeric(usacrime_df[‘Rape Crime Rate’], errors=‘coerce’)
usacrime_df[‘Assault Rate’] = pd.to_numeric(usacrime_df[‘Assault Rate’], errors=‘coerce’)
usacrime_df[‘Robbery Rate’] = pd.to_numeric(usacrime_df[‘Robbery Rate’], errors=‘coerce’)

Now, calculate the mean

usacrime_df[‘Crime Rate Average’] = usacrime_df[[‘Crime Rate’, ‘Homicide Rate’, ‘Rape Crime Rate’, ‘Assault Rate’, ‘Robbery Rate’]].mean(axis=1)

Drop the decimal by converting to integer

usacrime_df[‘Crime Rate Average’] = usacrime_df[‘Crime Rate Average’].astype(int)

#LOOKING AT TOTAL CRIME RATE AVERAGE AMOUGNST ALL STATES
#PLOT 2

Sort the DataFrame by Crime Rate Average in descending order

usacrime_df_sorted = usacrime_df.sort_values(by=‘Crime Rate Average’, ascending=False)

Get the top state with the highest Crime Rate Average

top_state = usacrime_df_sorted.iloc[0]

Plot the bar chart

plt.figure(figsize=(10, 6))
plt.bar(usacrime_df_sorted[‘States’], usacrime_df_sorted[‘Crime Rate Average’], color=‘grey’) # Plot all bars in grey
plt.bar(top_state[‘States’], top_state[‘Crime Rate Average’], color=‘red’, label=f’{top_state[“States”]} Crime Rate Average’) # Highlight the top state in red
plt.xlabel(‘States’)
plt.ylabel(‘Crime Rate Average Count’)
plt.title(‘Crime Rate Average by State (1975-2015)’)
plt.xticks(rotation=45, ha=‘right’)

Annotate Florida’s crime rate

plt.annotate(f’{top_state[“Crime Rate Average”]}',
xy=(top_state[‘States’], top_state[‘Crime Rate Average’]),
xytext=(0, 3),
textcoords=‘offset points’,
ha=‘center’,
fontsize=10,
color=‘black’) # Adjust xytext to position the annotation above the bar

plt.legend()
plt.show()
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#PLOT 3

#Sort the DataFrame by Crime Rate Average in descending order
usacrime_df_sorted = usacrime_df.sort_values(by=‘Crime Rate Average’, ascending=False)

Get the top state with the highest Crime Rate Average

top_state = usacrime_df_sorted.iloc[0]

Get the state with the lowest Crime Rate Average

lowest_state = usacrime_df_sorted.iloc[-1]

Plot the bar chart

plt.figure(figsize=(10, 6))

Plot all bars in grey

plt.bar(usacrime_df_sorted[‘States’], usacrime_df_sorted[‘Crime Rate Average’], color=‘grey’)

Highlight the top state in red

plt.bar(top_state[‘States’], top_state[‘Crime Rate Average’], color=‘red’, label=f’{top_state[“States”]} Crime Rate Average’)

Highlight the lowest state in blue

plt.bar(lowest_state[‘States’], lowest_state[‘Crime Rate Average’], color=‘blue’, label=f’{lowest_state[“States”]} Crime Rate Average’)

Annotate top state’s crime rate

plt.annotate(f’{top_state[“Crime Rate Average”]}',
xy=(top_state[‘States’], top_state[‘Crime Rate Average’]),
xytext=(0, 3),
textcoords=‘offset points’,
ha=‘center’,
fontsize=10,
color=‘black’)

Annotate lowest state’s crime rate

plt.annotate(f’{lowest_state[“Crime Rate Average”]}',
xy=(lowest_state[‘States’], lowest_state[‘Crime Rate Average’]),
xytext=(0, 3),
textcoords=‘offset points’,
ha=‘center’,
fontsize=10,
color=‘black’)

Annotate Virginia’s crime rate above the bar

plt.annotate(f’{lowest_state[“Crime Rate Average”]}',
xy=(lowest_state[‘States’], lowest_state[‘Crime Rate Average’]),
xytext=(0, 3),
textcoords=‘offset points’,
ha=‘center’,
fontsize=10,
color=‘black’)

plt.xlabel(‘States’)
plt.ylabel(‘Crime Rate Average Count’)
plt.title(‘Crime Rate Average by State (1975-2015)’)
plt.xticks(rotation=45, ha=‘right’)
plt.legend()
plt.show()

#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Filter the DataFrame to include only years between 2000 and 2015

usacrime_df_filtered = usacrime_df[(usacrime_df[‘Report Year’] >= 2000) & (usacrime_df[‘Report Year’] <= 2015)]

Sort the filtered DataFrame by Crime Rate in descending order to highlight the highest crime rate state

usacrime_df_sorted = usacrime_df_filtered.sort_values(by=‘Crime Rate’, ascending=False)

Select the state with the highest crime rate

highest_crime_state = usacrime_df_sorted.iloc[0]

Plotting

plt.figure(figsize=(12, 8))

Plot bars for all states in grey

plt.bar(usacrime_df_sorted[‘States’], usacrime_df_sorted[‘Crime Rate’], color=‘grey’)

Highlight the highest crime rate state (Florida) in red

plt.bar(highest_crime_state[‘States’], highest_crime_state[‘Crime Rate’], color=‘red’, label=‘Georiga’)

Annotate Florida’s crime rate on the graph

plt.text(highest_crime_state[‘States’], highest_crime_state[‘Crime Rate’], highest_crime_state[‘Crime Rate’],
ha=‘center’, va=‘bottom’)

plt.title(‘States with Crime Rates (2000-2015)’)
plt.xlabel(‘States’)
plt.ylabel(‘Crime Rate Count’)
plt.xticks(rotation=45, ha=‘right’) # Rotate x-axis labels for better readability
plt.tight_layout() # Adjust layout to prevent clipping of labels
plt.legend() # Show legend
plt.show()

#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

2222## Filter the DataFrame to include only years between 2000 and 2015

usacrime_df_filtered = usacrime_df[(usacrime_df[‘Report Year’] >= 2000) & (usacrime_df[‘Report Year’] <= 2015)]

Sort the filtered DataFrame by Crime Rate in descending order to highlight the highest crime rate state

usacrime_df_sorted = usacrime_df_filtered.sort_values(by=‘Crime Rate’, ascending=False)

Select the state with the highest crime rate

highest_crime_state = usacrime_df_sorted.iloc[0]

Select the state with the lowest crime rate

lowest_crime_state = usacrime_df_sorted.iloc[-1]

Plotting

plt.figure(figsize=(12, 8))

Plot bars for all states in grey

plt.bar(usacrime_df_sorted[‘States’], usacrime_df_sorted[‘Crime Rate’], color=‘grey’)

Highlight the highest crime rate state (Georgia) in red

plt.bar(highest_crime_state[‘States’], highest_crime_state[‘Crime Rate’], color=‘red’, label=‘Georgia’)

Annotate Georgia’s crime rate on the graph

plt.text(highest_crime_state[‘States’], highest_crime_state[‘Crime Rate’], highest_crime_state[‘Crime Rate’],
ha=‘center’, va=‘bottom’)

Plot bars for the lowest crime rate state (Virginia) in blue

plt.bar(lowest_crime_state[‘States’], lowest_crime_state[‘Crime Rate’], color=‘blue’, label=‘Virginia’)

Annotate Virginia’s crime rate on the graph

plt.text(lowest_crime_state[‘States’], lowest_crime_state[‘Crime Rate’], lowest_crime_state[‘Crime Rate’],
ha=‘center’, va=‘bottom’)

plt.title(‘States with Crime Rates (2000-2015)’)
plt.xlabel(‘States’)
plt.ylabel(‘Crime Rate Count’)
plt.xticks(rotation=45, ha=‘right’) # Rotate x-axis labels for better readability
plt.tight_layout() # Adjust layout to prevent clipping of labels
plt.legend() # Show legend
plt.show()