id
stringlengths
5
72
modality
stringclasses
1 value
text
stringlengths
57
605
image
imagewidth (px)
d_multi_col_60080
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Yes"], x_values_bbox)
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d_multi_col_60099
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Stopped"], x_values_bbox)
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d_multi_col_60104
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2006"], x_values_bbox)
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d_multi_col_60127
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2014"], x_values_bbox)
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d_multi_col_60129
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Apple", "Samsung"], x_values_bbox)
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d_multi_col_60157
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ['Kidney', 'Liver', 'Heart', 'Pancreas'], x_values_bbox)
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d_multi_col_60164
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2016/17"], x_values_bbox)
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d_multi_col_60178
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["March 6", "March 16", "March 27"], x_values_bbox)
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d_multi_col_60318
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Senior compliance manager"], x_values_bbox)
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d_multi_col_60341
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# The x value to focus on is "2020" # The values for 2020 are: 65.43% (dark blue), 20.16% (gray), and 14.41% (light blue) # Excluding the dark blue bar, the sum is 20.16% + 14.41% sum_excluding_dark_blue = 20.16 + 14.41 ANSWER: The sum of 2020 excluding the dark blue bar value is 34.57%.
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d_multi_col_60448
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x_values_to_focus_on = ['2018', '2019', '2020'] image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, x_values_to_focus_on, x_values_bbox)
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d_multi_col_60471
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["UK & Ireland", "Rest of Europe"], x_values_bbox)
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d_multi_col_60482
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["With other U.S. Asian"], x_values_bbox)
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d_multi_col_60491
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2019", "2020"], x_values_bbox)
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d_multi_col_60500
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Rather support"], x_values_bbox)
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d_multi_col_60509
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ['Very satisfied', 'Somewhat satisfied', 'Somewhat dissatisfied', 'Very dissatisfied'], x_values_bbox)
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d_multi_col_60528
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2012"], x_values_bbox)
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d_multi_col_60554
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Including partnered streamers"], x_values_bbox)
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d_multi_col_60569
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["25-34 year", "35-44 year"], x_values_bbox)
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d_multi_col_60577
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Newly qualified", "Qualified (2-5 years)"], x_values_bbox)
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d_multi_col_60638
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Ten years ago", "Now"], x_values_bbox)
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d_multi_col_60644
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Phase I to phase II"], x_values_bbox)
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d_multi_col_60670
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# Highlight the red bars for "5 to 7 hours" across all age groups image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ['18-29', '30-44', '45-54', '55-64', '65+'], x_values_bbox)
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d_multi_col_60671
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Social media"], x_values_bbox)
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d_multi_col_60710
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Netherlands"], x_values_bbox)
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d_multi_col_60731
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Development"], x_values_bbox)
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d_multi_col_60736
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# The blue bars correspond to March. Let's focus on these values. x_values_to_focus_on = ["We'll spend more", "We'll spend less", "I don't think it will", "I'm not sure"] image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, x_values_to_focus_on, x_values_bbox)
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d_multi_col_60767
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ['Any psychotropic drug*', 'Antidepressants*', 'ADHD drugs*'], x_values_bbox)
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d_multi_col_60787
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Tablet"], x_values_bbox)
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d_multi_col_60828
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["5-6 times a"], x_values_bbox)
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d_multi_col_60833
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Yes, games downloaded", "Yes, educational content"], x_values_bbox)
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d_multi_col_60834
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2011", "2012"], x_values_bbox)
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d_multi_col_60850
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Air Force"], x_values_bbox)
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d_multi_col_60852
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["USA"], x_values_bbox)
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d_multi_col_60861
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ['2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020'], x_values_bbox)
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d_multi_col_60862
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Killed", "Wounded"], x_values_bbox)
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d_multi_col_60875
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["11-15 years"], x_values_bbox)
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d_multi_col_60899
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Blacks"], x_values_bbox)
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d_multi_col_60910
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Cable or satellite"], x_values_bbox)
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d_multi_col_60938
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Will get better", "Will get worse"], x_values_bbox)
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d_multi_col_60989
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Polished diamond value in jewelry", "Diamond jewelry retail sales"], x_values_bbox)
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d_multi_col_80033
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Primary school"], x_values_bbox)
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d_multi_col_80064
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2011", "2012", "2013"], x_values_bbox)
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d_multi_col_80073
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2009", "2010"], x_values_bbox)
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d_multi_col_80080
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2019"], x_values_bbox)
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d_multi_col_80081
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Teaching"], x_values_bbox)
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d_multi_col_80087
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["18-29"], x_values_bbox)
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d_multi_col_80099
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["50-64"], x_values_bbox)
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d_multi_col_80103
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Domestic travel", "International travel"], x_values_bbox)
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d_multi_col_80129
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2017"], x_values_bbox)
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d_multi_col_80179
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# Focus on the x values where the opposing party approval is more than 45% x_values_to_focus_on = ['Dwight D. Eisenhower', 'John F. Kennedy', 'George H.W. Bush'] image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, x_values_to_focus_on, x_values_bbox)
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d_multi_col_80184
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2015/16"], x_values_bbox)
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d_multi_col_80210
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Cars", "Trucks"], x_values_bbox)
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d_multi_col_80280
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ['2009*', '2010*'], x_values_bbox)
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d_multi_col_80314
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2016"], x_values_bbox)
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d_multi_col_100016
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Yes", "No"], x_values_bbox)
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d_multi_col_100019
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Sept 18-19"], x_values_bbox)
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d_multi_col_100083
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2-6 years"], x_values_bbox)
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d_multi_col_100093
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Liver"], x_values_bbox)
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d_multi_col_100094
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["1985"], x_values_bbox)
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d_multi_col_100105
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# Highlight the x values to focus on the changes in followers image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ['Protestant/Other', 'Catholic', 'Mormon', 'Jewish', 'Muslim', 'Other non-', 'None/Atheist/Agnostic', 'No response'], x_values_bbox)
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d_multi_col_100109
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Standard bikini line", "Full bikini line"], x_values_bbox)
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d_multi_col_100143
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Used grocery delivery", "Intends to use grocery delivery"], x_values_bbox)
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d_multi_col_100180
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Scotland"], x_values_bbox)
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d_multi_col_100182
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# Since I need to check the difference for each area, I will focus on all x values. image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ['London', 'London', 'Cambridge', 'Edinburgh', 'Manchester', 'Oxford'], x_values_bbox)
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d_multi_col_100223
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Positive", "Negative"], x_values_bbox)
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d_multi_col_100235
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Every day"], x_values_bbox)
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d_multi_col_100249
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Travel and hotels", "Electronic items", "Books", "Music -", "Home"], x_values_bbox)
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d_multi_col_100305
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2018", "2019"], x_values_bbox)
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d_multi_col_80331
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Overweight"], x_values_bbox)
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d_multi_col_80364
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["1 symptom", "2 or more symptoms"], x_values_bbox)
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d_multi_col_80406
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2017", "2018", "2019"], x_values_bbox)
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d_multi_col_80653
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# Highlight the blue bar data to identify the values divisible by 9 image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ['Europe', 'Germany', 'France', 'UK', 'Italy', 'Spain', 'Netherlands', 'Switzerland', 'Ireland'], x_values_bbox)
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d_multi_col_80686
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["2008", "2009"], x_values_bbox)
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d_multi_col_80812
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["YouTube", "BabyCenter Community"], x_values_bbox)
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d_multi_col_81012
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Regular"], x_values_bbox)
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d_multi_col_81015
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Low-income", "Middle-income"], x_values_bbox)
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d_two_col_48
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Average deaths/day by all", "Average deaths/day by"], x_values_bbox)
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d_two_col_85
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image_with_focused_x_values = focus_on_x_values_with_highlight(image_1, ["Under 15,000", "15,000 to 24,999"], x_values_bbox)
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