Ethnobiological data assessment tool
This tool is designed to help researchers analyze their ethnobiological data. It is based on the ethnobotanyR package with some modifications. Also, for further information on the methods used in this tool, please refer to the ethnobotanyR package.
Scientific explanations of the indices used in this tool can be found in the documentation of the ethnobotanyR package.
Please check also citiation part if you use this package or ethnobotanyR.
To install this package, you can use the following code:
pip install pyethnobiologyWe recommend use virtualenv to install this package.
import pandas as pd
from pyethnobiology import pyethnobiologyThis package accepts data in the following formats:
- A data frame with the following columns: informant, taxon, uses. The use columns must be binary (0 or 1). For example:
| informant | sp_name | Use_1 | Use_2 | Use_3 | Use_4 | Use_5 |
|---|---|---|---|---|---|---|
| inform_a | sp_a | 0 | 0 | 0 | 1 | 1 |
| inform_a | sp_b | 0 | 0 | 0 | 0 | 0 |
| inform_a | sp_c | 0 | 0 | 1 | 0 | 0 |
| inform_a | sp_d | 0 | 0 | 0 | 0 | 0 |
| inform_b | sp_a | 0 | 1 | 0 | 0 | 1 |
- A data frame with the following columns: informant, taxon, use. The use column must be a string. For example:
| informant | sp_name | ailments_treated |
|---|---|---|
| inform_a | sp_a | Use_3 |
| inform_a | sp_a | Use_8 |
| inform_a | sp_a | Use_9 |
| inform_a | sp_c | Use_6 |
| inform_a | sp_c | Use_7 |
| inform_b | sp_d | Use_3 |
| inform_b | sp_d | Use_6 |
| inform_b | sp_d | Use_7 |
| inform_b | sp_d | Use_8 |
- Literature data is optional for Jaccard index calculation. If there is a multiple literatures, they must be separated by a semicolon. For example:
| informant | sp_name | ailments_treated | literature |
|---|---|---|---|
| inform_a | sp_a | Use_3 | 1 |
| inform_a | sp_a | Use_8 | 1;2 |
| inform_a | sp_a | Use_9 | 1;3;5 |
| inform_a | sp_c | Use_6 | 2 |
| inform_a | sp_c | Use_7 | 1;7 |
| inform_b | sp_d | Use_3 | 3 |
To use this package, you can use the following code:
# You can define your separator, for example: sep=";" or sep="\t". By default, the separator is ",".
data = pd.read_csv("{path}/data.csv") # or pd.read_excel("data.xlsx") or pd.read_csv("data.txt")data = "{path}/data.rda"If uses are binary values in your data (first example in accepted data formats), you don't need to define the use column. But if uses are strings (second example in accepted data formats), you have to define the use column. For example:
# if uses are binary values
informant_column = "informant"
taxon_column = "sp_name"
convert_use_data = True# if uses are strings
informant_column = "informant"
taxon_column = "sp_name"
use_column = "ailments_treated"# if you have literature data
informant_column = "informant"
taxon_column = "sp_name"
use_column = "ailments_treated"
literature_column = "literature"pye = pyethnobiology(data, informant_column, taxon_column, convert_use_data)You can also define the use column and literature column in the class instance. For example:
pye = pyethnobiology(data, informant_column="informant", taxon_column="sp_name", use_column="ailments_treated")pye.UR().calculate()
Out[1]:
sp_name UR
0 sp_c 52
1 sp_a 43
2 sp_d 43
3 sp_b 36pye.CI().calculate()
Out[1]:
sp_name CI
0 sp_c 2.60
1 sp_a 2.15
2 sp_d 2.15
3 sp_b 1.80pye.FC().calculate()
Out[1]:
sp_name FC
0 sp_c 17
1 sp_a 15
2 sp_b 12
3 sp_d 12pye.NU().calculate()
Out[1]:
sp_name NU
0 sp_c 8
1 sp_d 8
2 sp_a 7
3 sp_b 7pye.RFC().calculate()
Out[1]:
sp_name RFC
0 sp_c 0.85
1 sp_a 0.75
2 sp_b 0.60
3 sp_d 0.60pye.RI().calculate()
Out[1]:
sp_name RI
0 sp_c 1.000000
1 sp_a 0.878676
2 sp_d 0.852941
3 sp_b 0.790441pye.UV().calculate()
Out[1]:
sp_name UV
0 sp_c 2.60
1 sp_a 2.15
2 sp_d 2.15
3 sp_b 1.80pye.CV().calculate()
Out[1]:
sp_name CV
0 sp_c 1.76800
2 sp_a 1.12875
1 sp_d 1.03200
3 sp_b 0.75600pye.FL().calculate()
Out[1]:
sp_name ailments_treated FL
1 sp_a Use_10 20.000000
2 sp_a Use_2 53.333333
3 sp_a Use_3 60.000000
5 sp_a Use_5 33.333333
6 sp_a Use_6 46.666667
8 sp_a Use_8 40.000000
9 sp_a Use_9 33.333333
10 sp_b Use_1 50.000000
13 sp_b Use_3 41.666667
14 sp_b Use_4 33.333333
15 sp_b Use_5 33.333333
16 sp_b Use_6 41.666667
17 sp_b Use_7 58.333333
19 sp_b Use_9 41.666667
20 sp_c Use_1 52.941176
21 sp_c Use_10 35.294118
22 sp_c Use_2 41.176471
24 sp_c Use_4 35.294118
25 sp_c Use_5 17.647059
26 sp_c Use_6 23.529412
27 sp_c Use_7 29.411765
28 sp_c Use_8 70.588235
30 sp_d Use_1 41.666667
32 sp_d Use_2 25.000000
33 sp_d Use_3 58.333333
35 sp_d Use_5 8.333333
36 sp_d Use_6 41.666667
37 sp_d Use_7 66.666667
38 sp_d Use_8 75.000000
39 sp_d Use_9 41.666667Informant Consensus Factor (FIC) operates like a consensus barometer, assessing the agreement among individuals. It boils down to a simple equation:
where Nur is the number of use reports for a particular use category and Nt is the number of taxa used for that category. The FIC ranges from 0 to 1, with 0 indicating no agreement among informants and 1 indicating that all informants agree on the taxa to be used for a particular category (Heinrich et al., 1998; Güzel et al., 2015).
pye.FIC().calculate()
Out[1]:
ailments_treated FIC
0 Use_8 0.923077
1 Use_3 0.900000
2 Use_7 0.894737
3 Use_1 0.894737
4 Use_4 0.888889
5 Use_2 0.882353
6 Use_10 0.875000
7 Use_9 0.857143
8 Use_6 0.850000
9 Use_5 0.750000Jaccard similarity index is a measure of similarity between two sets of data. The higher the Jaccard index, the more similar the two samples are. You can calculate the Jaccard index for all studies or for each study. For example:
pye.jaccard()
Out[1]:
study similarity
0 2 0.480000
1 3 0.480000
2 5 0.480000
3 6 0.440000
4 1 0.400000
5 4 0.360000
6 7 0.360000
7 9 0.200000
8 10 0.035714
9 8 0.000000Mean similarity of all studies:
pye.jaccard()['similarity'].mean()
Out[1]:
0.32357142857142857
# Round to 2 decimal places
pye.jaccard()['similarity'].mean().round(2)
Out[1]:
0.32You can plot all indices in a radial plot except Fidelity Level (FL). For example:
#Use Report (UR) per species
pye.UR().plot_radial()
You can customize the radial plot. For example:
#Use Report (UR) per species
pye.UR().plot_radial(filename="UR.png", # You can define the saving filename. It is default to "UR.png" for UR index.
dpi=300, # You can define the dpi. It is default to 300.
num_row=10, # It is default to 10 which is the first 10 rows of the data.
ytick_position="onbar", # You can define the ytick position. It is default to "onbar".
colors=None, # You can define the colors. It is default to None.
show_colorbar=True # You can define if you want to show the colorbar. It is default to True.
)num_row means the number of species you want to plot. For example, if you want to plot the first 5 species of the data, you can use the following code:
#Use Report (UR) per species
pye.UR().plot_radial(num_row=5)You can change the colors of the radial plot. It takes the range of two colors. For example:
#Use Report (UR) per species
pye.UR().plot_radial(colors=["#ECF4D6", "#265073"]) # [minimum value color, maximum value color]
You can also use multiple configurations. For example:
#Use Report (UR) per species
pye.UR().plot_radial(filename="UR_conf.png",
dpi=300,
num_row=5,
ytick_position="onbar", # on_line
colors=["#ECF4D6", "#265073"],
show_colorbar=False
)
You can only plot the Fidelity Level (FL) index in a heatmap. For example:
#Fidelity Level (FL) per species
pye.FL().plot_heatmap()
You can customize the heatmap. For example:
#Fidelity Level (FL) per species
pye.FL().plot_heatmap(
filename="FL.png", # You can define the saving filename. It is default to "FL.png"
cmap="coolwarm", # You can define the cmap. It is default to "coolwarm".
show_colorbar=True, # You can define if you want to show the colorbar. It is default to True.
colorbar_shrink=0.50, # You can define the colorbar shrink. It is default to 0.50.
plot_width=10, # You can define the plot width. It is default to 10.
plot_height=8, # You can define the plot height. It is default to 8.
dpi=300, # You can define the dpi. It is default to 300.
fillna_zero=True # You can define if you want to fill the NaN values with 0. It is default to True.
)
)Supported cmaps: https://matplotlib.org/stable/tutorials/colors/colormaps.html
#Fidelity Level (FL) per species
pye.FL().plot_heatmap(filename="FL_reds.png",cmap="Reds")
You can plot all data with the Chord plot. It is diagram of ethnobiological uses and species. For example:
pye.plot_chord()
You can customize the Chord plot. For example:
pye.plot_chord(filename="chord_plot.png", # You can define the saving filename. It is default to "chord_plot.png"
dpi=300, # You can define the dpi. It is default to 300.
by="taxon", # You can define the by. It is default to "taxon". Other option is "informant".
colors=None, # You can define the colors. It is default to None.
min_info_count=0, # You can define the minimum information count for filter data. It is default to 0.
get_first=None # You can define the number of first data. It is default to None.
)You can change the colors of the Chord plot. Supported cmaps: https://matplotlib.org/stable/tutorials/colors/colormaps.html
pye.plot_chord(colors="Dark2")
You can also use multiple configurations. For example:
pye.plot_chord(filename="chord_plot_conf.png",
dpi=300,
by="informant",
colors="tab20b",
min_info_count=5,
get_first=10
)
Albuquerque, Ulysses Paulino, Patricia Muniz de Medeiros, Washington Soares Ferreira Junior, Taline Cristina da Silva, Rafael Ricardo Vasconcelos da Silva, and Thiago Goncalves-Souza. 2019. Social-Ecological Theory of Maximization: Basic Concepts and Two Initial Models. Biological Theory. doi.org/10.1007/s13752-019-00316-8.
Heinrich, M., Ankli, A., Frei, B., Weimann, C., & Sticher, O. (1998). Medicinal plants in Mexico: Healers consensus and cultural importance. Social science & medicine, 47(11), 1859-1871.
Tardio, J., and M. Pardo-de-Santayana, (2008). Cultural Importance Indices: A Comparative Analysis Based on the Useful Wild Plants of Southern Cantabria (Northern Spain) 1. Economic Botany, 62(1), 24-39. doi.org/10.1007/s12231-007-9004-5.
Whitney, C. W., Bahati, J., and Gebauer, J. (2018). Ethnobotany and agrobiodiversity; valuation of plants in the homegardens of southwestern Uganda. Ethnobiology Letters, 9(2), 90-100. doi.org/10.14237/ebl.9.2.2018.503.
Whitney C. W. (2022). ethnobotanyR: Calculate Quantitative Ethnobotany Indices. R package version 0.1.9, https://CRAN.R-project.org/package=ethnobotanyR..
APA
Demir, O., & Whitney, C. (2023). pyethnobiology: Ethnobiological data assessment tool (Version 0.1.1) [Software]. https://github.com/demirogun/pyethnobiology
BibTeX
@software{pyethnobiology,
author = {Ogün Demir and Cory Whitney},
title = {{pyethnobiology}},
version = {0.1.1},
url = {https://github.com/demirogun/pyethnobiology},
year = {2023}
}
citation("ethnobotanyR")
#>
#> To cite package 'ethnobotanyR' in publications use:
#>
#> Whitney C (2022). _ethnobotanyR: Calculate Quantitative Ethnobotany
#> Indices_. R package version 0.1.9,
#> <https://CRAN.R-project.org/package=ethnobotanyR>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {ethnobotanyR: Calculate Quantitative Ethnobotany Indices},
#> author = {Cory Whitney},
#> year = {2022},
#> note = {R package version 0.1.9},
#> url = {https://CRAN.R-project.org/package=ethnobotanyR},
#> }