TidyTuesday dataset of September 2, 2025

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

frogID_data = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-02/frogID_data.csv')
frog_names = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-02/frog_names.csv')

frogID_data

	occurrenceID	eventID	decimalLatitude	decimalLongitude	scientificName	eventDate	eventTime	timezone	coordinateUncertaintyInMeters	recordedBy	stateProvince
0	12832	525618	-28.500000	153.100000	Philoria loveridgei	2023-01-01	11:18:32	GMT+1100	10000.000000	41480	New South Wales
1	12833	526341	-33.700000	151.200000	Heleioporus australiacus	2023-01-02	20:39:30	GMT+1100	10000.000000	834983	New South Wales
2	12834	526673	-28.700000	152.700000	Mixophyes iteratus	2023-01-02	21:30:07	GMT+1100	10000.000000	804177	New South Wales
3	12835	526673	-28.700000	152.700000	Mixophyes fasciolatus	2023-01-02	21:30:07	GMT+1100	10000.000000	804177	New South Wales
4	12836	526673	-28.700000	152.700000	Litoria latopalmata	2023-01-02	21:30:07	GMT+1100	10000.000000	804177	New South Wales
...	...	...	...	...	...	...	...	...	...	...	...
136616	974110	619213	-35.126800	150.753000	Crinia signifera	2023-11-09	17:29:47	GMT+1100	8.000000	825385	Other Territories
136617	974111	658692	-35.152206	150.759737	Crinia signifera	2023-03-21	11:59:24	GMT+1100	4.741598	12486	Other Territories
136618	974112	691758	-35.125944	150.755366	Limnodynastes peronii	2023-08-25	11:36:58	GMT+1000	11.824237	826303	Other Territories
136619	974118	802152	-35.126700	150.755000	Paracrinia haswelli	2023-06-06	11:16:39	GMT+1000	4.357000	35183	Other Territories
136620	974119	802152	-35.126700	150.755000	Crinia signifera	2023-06-06	11:16:39	GMT+1000	4.357000	35183	Other Territories

136621 rows × 11 columns

frog_names

	subfamily	tribe	scientificName	commonName	secondary_commonNames
0	Hylid	Pelodryadidae	Cyclorana	—	—
1	Hylid	Pelodryadidae	Cyclorana alboguttata	Striped Burrowing Frog	Green-striped Frog
2	Hylid	Pelodryadidae	Cyclorana australis	Northern Snapping Frog	Giant Frog
3	Hylid	Pelodryadidae	Cyclorana brevipes	Superb Collared Frog	Short-footed Frog
4	Hylid	Pelodryadidae	Cyclorana cryptotis	Hidden-ear Frog	Earless Frog
...	...	...	...	...	...
289	Myobatrachid	Myobatrachidae	Uperoleia tyleri	Tyler’s Toadlet	—
290	Ranid	Ranidae	Papurana	No widely used common name for the genus	—
291	Ranid	Ranidae	Papurana daemeli	Wood Frog	Australian Wood Frog
292	Toad	Bufonidae	Rhinella	South American Toads	—
293	Toad	Bufonidae	Rhinella marina	Cane Toad	—

294 rows × 5 columns

frogID_data['genus'] = frogID_data['scientificName'].str.split(' ').str[0]
frogID_data['species'] = frogID_data['scientificName'].str.split(' ').str[1]
frogID_data

	occurrenceID	eventID	decimalLatitude	decimalLongitude	scientificName	eventDate	eventTime	timezone	coordinateUncertaintyInMeters	recordedBy	stateProvince	genus	species
0	12832	525618	-28.500000	153.100000	Philoria loveridgei	2023-01-01	11:18:32	GMT+1100	10000.000000	41480	New South Wales	Philoria	loveridgei
1	12833	526341	-33.700000	151.200000	Heleioporus australiacus	2023-01-02	20:39:30	GMT+1100	10000.000000	834983	New South Wales	Heleioporus	australiacus
2	12834	526673	-28.700000	152.700000	Mixophyes iteratus	2023-01-02	21:30:07	GMT+1100	10000.000000	804177	New South Wales	Mixophyes	iteratus
3	12835	526673	-28.700000	152.700000	Mixophyes fasciolatus	2023-01-02	21:30:07	GMT+1100	10000.000000	804177	New South Wales	Mixophyes	fasciolatus
4	12836	526673	-28.700000	152.700000	Litoria latopalmata	2023-01-02	21:30:07	GMT+1100	10000.000000	804177	New South Wales	Litoria	latopalmata
...	...	...	...	...	...	...	...	...	...	...	...	...	...
136616	974110	619213	-35.126800	150.753000	Crinia signifera	2023-11-09	17:29:47	GMT+1100	8.000000	825385	Other Territories	Crinia	signifera
136617	974111	658692	-35.152206	150.759737	Crinia signifera	2023-03-21	11:59:24	GMT+1100	4.741598	12486	Other Territories	Crinia	signifera
136618	974112	691758	-35.125944	150.755366	Limnodynastes peronii	2023-08-25	11:36:58	GMT+1000	11.824237	826303	Other Territories	Limnodynastes	peronii
136619	974118	802152	-35.126700	150.755000	Paracrinia haswelli	2023-06-06	11:16:39	GMT+1000	4.357000	35183	Other Territories	Paracrinia	haswelli
136620	974119	802152	-35.126700	150.755000	Crinia signifera	2023-06-06	11:16:39	GMT+1000	4.357000	35183	Other Territories	Crinia	signifera

136621 rows × 13 columns

frogID_data.groupby('genus').nunique()['species'].sort_values(ascending=False)

genus
Litoria            64
Uperoleia          21
Crinia             14
Cyclorana          11
Pseudophryne       10
Limnodynastes      10
Mixophyes           8
Cophixalus          7
Neobatrachus        6
Heleioporus         6
Austrochaperina     5
Geocrinia           4
Philoria            4
Platyplectrum       2
Notaden             2
Anstisia            2
Myobatrachus        1
Papurana            1
Paracrinia          1
Metacrinia          1
Lechriodus          1
Assa                1
Rhinella            1
Spicospina          1
Taudactylus         1
Adelotus            1
Name: species, dtype: int64

frogID_data['eventDate'] = pd.to_datetime(frogID_data['eventDate'])
df_grp = frogID_data.groupby('eventDate').nunique().reset_index()
df_grp

	eventDate	occurrenceID	eventID	decimalLatitude	decimalLongitude	scientificName	eventTime	timezone	coordinateUncertaintyInMeters	recordedBy	stateProvince	genus	species
0	2023-01-01	1042	574	518	476	66	571	5	413	349	8	17	62
1	2023-01-02	722	398	372	347	62	398	5	292	257	8	15	58
2	2023-01-03	438	268	251	234	51	267	5	219	194	8	12	49
3	2023-01-04	604	397	367	351	59	392	5	310	281	9	13	56
4	2023-01-05	941	485	419	394	66	483	5	369	300	8	16	62
...	...	...	...	...	...	...	...	...	...	...	...	...	...
308	2023-11-05	3405	1981	1761	1588	65	1946	5	1403	1089	9	15	61
309	2023-11-06	4253	2155	1960	1760	66	2103	5	1517	1193	9	15	60
310	2023-11-07	3601	1864	1721	1561	64	1828	5	1334	1077	8	14	61
311	2023-11-08	3540	1755	1622	1476	72	1718	5	1292	1003	8	14	68
312	2023-11-09	3200	1512	1398	1294	64	1480	5	1116	844	9	15	59

313 rows × 13 columns

aus_seasons = {'Summer': ('2023-01-01', '2023-02-28'),
               'Autumn': ('2023-03-01', '2023-05-31'),
               'Winter': ('2023-06-01', '2023-08-31'),
               'Spring': ('2023-09-01', '2023-11-30')}
fig, ax = plt.subplots(figsize=(8, 4))
plt.rcParams['figure.facecolor'] = '#FFCA99'  # Dark grey figure background
plt.rcParams['axes.facecolor'] = '#FFCA99'  
plt.rcParams['axes.edgecolor'] = '#D69456'
plt.rcParams['xtick.color'] = '#713600'
plt.rcParams['ytick.color'] = '#713600'
plt.rcParams['text.color'] = '#713600'
plt.rcParams['axes.labelcolor'] = '#713600'
plt.rcParams['axes.titlecolor'] = '#713600'


for season, (start, end) in aus_seasons.items():
    if (season == 'Autumn' or season == 'Spring'):
        ax.axvspan(pd.to_datetime(start), pd.to_datetime(end), color='#FFE5B4', alpha=0.3, lw=0)
    ax.text((pd.to_datetime(end) - pd.to_datetime(start)) / 2 + pd.to_datetime(start), 10, season, fontsize=10, ha='center', va='bottom', color='#713600')

sns.scatterplot(data=df_grp, x='eventDate', y='scientificName',alpha=0.75, hue='eventID', \
palette='Greens', edgecolor='none', size='eventID', sizes=(10,100), ax=ax)
sns.despine()
#handles, labels = ax.get_legend_handles_labels()
#ax.legend(handles[::-1], labels[::-1], frameon=False, loc='upper center', title='Sighting Events', title_fontsize='medium')
ax.legend(frameon=False, loc='upper center', title='Sighting Events', title_fontsize='medium')
plt.xlabel('')
plt.ylabel('Number of Species')
plt.title('Timeline of frog sightings in Australia in 2023. \nMost species breed during the rainy season, leading to a \npeak in sightings.', fontsize=14, fontfamily="Serif", \
ha='left', x=0, y=1)

dates = pd.date_range(start='2023-01-01', end='2023-12-31', freq='MS')
labels = ['Jan', '', 'Mar', '', '', 'Jun', '', '', 'Sep', '', '', 'Dec']
# Filter out blank labels
filtered_dates = [date for date, label in zip(dates, labels) if label != '']
filtered_labels = [label for label in labels if label != '']
plt.xticks(filtered_dates, filtered_labels)

#plt.xticks(pd.date_range(start='2023-01-01', end='2023-12-31', freq='MS'), ['Jan', '', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
plt.savefig("Frogs_AUS.png", dpi=300, bbox_inches='tight')
plt.show()

# count commonName values having Tree in it.
print(frog_names['commonName'].str.contains('Tree').sum())
print(frog_names['commonName'].str.contains('Green').sum())

46
6

from wordcloud import WordCloud
all_cnames = ' '.join(frog_names['commonName'])
# remove frog from all_cnames
all_cnames = all_cnames.replace('Frog', '')
wordcloud = WordCloud(width = 800, height = 800, background_color ='white').generate(all_cnames)

plt.figure(figsize = (8, 8), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)