248 lines
5.6 KiB
Plaintext
248 lines
5.6 KiB
Plaintext
```{python}
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#| echo: false
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import matplotlib.pyplot as plt
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def prepare_plot_colors():
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# "Tableau 20" colors as RGB.
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colors = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
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(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
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(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
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(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
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(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
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# Scale RGB values to the [0, 1] range for matplotlib
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for i in range(len(colors)):
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r, g, b = colors[i]
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colors[i] = (r / 255., g / 255., b / 255.)
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return colors
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colors=prepare_plot_colors()
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```
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```{python}
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#| echo: false
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import openpyxl
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import pandas as pd
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df = pd.read_csv('data/cleaned/UNU-WIDER-WIID/WIID-30JUN2022_cty-select.csv', index_col="id", parse_dates=True)
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```
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```{python}
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#| echo: false
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df = df.loc[df['year'] > 1990]
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ben = df.loc[df['c3'] == "BEN"]
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dji = df.loc[df['c3'] == "DJI"]
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uga = df.loc[df['c3'] == "UGA"]
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vnm = df.loc[df['c3'] == "VNM"]
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```
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```{python}
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from IPython.display import Markdown
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from tabulate import tabulate
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```
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```{python}
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# Set up the data extraction and figure drawing functions
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import plotly.express as px
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import plotly.io as pio
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def gini_plot(country_df):
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if svg_render:
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pio.renderers.default = "png"
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fig = px.line(country_df, x="year", y="gini", markers=True, labels={"year": "Year", "gini": "Gini coefficient"}, template="seaborn", range_y=[0,100])
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fig.update_traces(marker_size=10)
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fig.show()
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def plot_consumption_gini_percapita(country_df):
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gni_cnsmpt = country_df[country_df['resource'].str.contains("Consumption")]
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gni_cnsmpt_percapita = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
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gini_plot(gni_cnsmpt_percapita)
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def plot_consumption_gini_percapita_ruralurban(country_df):
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gni_cnsmpt = country_df[country_df['resource'].str.contains("Consumption")]
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gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
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gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['source'].str.contains("World Bank")]
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gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['areacovr'].str.contains("All")]
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gini_plot(gni_cnsmpt)
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```
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```{python}
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## Set up functions to grab development aids by type of donating body
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## ODA donor type map, see DAC code sheet xlsx
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donortypes = {
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1: 'dac',
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2: 'dac',
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3: 'dac',
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4: 'dac',
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5: 'dac',
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6: 'dac',
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7: 'dac',
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8: 'dac',
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9: 'dac',
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10: 'dac',
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11: 'dac',
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12: 'dac',
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18: 'dac',
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20: 'dac',
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21: 'dac',
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22: 'dac',
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40: 'dac',
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50: 'dac',
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61: 'dac',
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68: 'dac',
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69: 'dac',
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75: 'dac',
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76: 'dac',
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301: 'dac',
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302: 'dac',
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701: 'dac',
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742: 'dac',
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801: 'dac',
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820: 'dac',
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918: 'dac',
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104: 'mlt',
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807: 'mlt',
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811: 'mlt',
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812: 'mlt',
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901: 'mlt',
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902: 'mlt',
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903: 'mlt',
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905: 'mlt',
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906: 'mlt',
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907: 'mlt',
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909: 'mlt',
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913: 'mlt',
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914: 'mlt',
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915: 'mlt',
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921: 'mlt',
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923: 'mlt',
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926: 'mlt',
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928: 'mlt',
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932: 'mlt',
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940: 'mlt',
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944: 'mlt',
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948: 'mlt',
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951: 'mlt',
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952: 'mlt',
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953: 'mlt',
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954: 'mlt',
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956: 'mlt',
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958: 'mlt',
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959: 'mlt',
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960: 'mlt',
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963: 'mlt',
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964: 'mlt',
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966: 'mlt',
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967: 'mlt',
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971: 'mlt',
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974: 'mlt',
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976: 'mlt',
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978: 'mlt',
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979: 'mlt',
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980: 'mlt',
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981: 'mlt',
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982: 'mlt',
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983: 'mlt',
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988: 'mlt',
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990: 'mlt',
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992: 'mlt',
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997: 'mlt',
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1011: 'mlt',
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1012: 'mlt',
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1013: 'mlt',
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1014: 'mlt',
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1015: 'mlt',
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1016: 'mlt',
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1017: 'mlt',
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1018: 'mlt',
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1019: 'mlt',
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1020: 'mlt',
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1023: 'mlt',
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1024: 'mlt',
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1025: 'mlt',
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1037: 'mlt',
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1038: 'mlt',
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1311: 'mlt',
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1312: 'mlt',
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1313: 'mlt',
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30: 'nondac',
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45: 'nondac',
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55: 'nondac',
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62: 'nondac',
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70: 'nondac',
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72: 'nondac',
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77: 'nondac',
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82: 'nondac',
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83: 'nondac',
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84: 'nondac',
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87: 'nondac',
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130: 'nondac',
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133: 'nondac',
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358: 'nondac',
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543: 'nondac',
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546: 'nondac',
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552: 'nondac',
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561: 'nondac',
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566: 'nondac',
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576: 'nondac',
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611: 'nondac',
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613: 'nondac',
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732: 'nondac',
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764: 'nondac',
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765: 'nondac',
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1601: 'private',
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1602: 'private',
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1603: 'private',
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1604: 'private',
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1605: 'private',
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1606: 'private',
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1607: 'private',
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1608: 'private',
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1609: 'private',
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1610: 'private',
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1611: 'private',
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1612: 'private',
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1613: 'private',
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1614: 'private',
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1615: 'private',
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1616: 'private',
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1617: 'private',
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1618: 'private',
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1619: 'private',
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1620: 'private',
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1621: 'private',
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1622: 'private',
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1623: 'private',
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1624: 'private',
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1625: 'private',
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1626: 'private',
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1627: 'private',
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1628: 'private',
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1629: 'private',
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1630: 'private',
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1631: 'private',
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1632: 'private',
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1633: 'private',
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1634: 'private',
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1635: 'private',
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1636: 'private',
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1637: 'private',
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1638: 'private',
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1639: 'private',
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}
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def totals_by_donortype(oda_frame):
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totals = oda_frame.loc[
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(df['RECIPIENT'] == 236) &
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(df['SECTOR'] == 1000) &
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(df['FLOW'] == 100) &
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(df['CHANNEL'] == 100) &
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(df['AMOUNTTYPE'] == 'D') &
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(df['FLOWTYPE'] == 112) &
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(df['AIDTYPE'] == "100") # contains mixed int and string representations
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]
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donortotals = totals.copy()
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donortotals["Donortype"] = donortotals["DONOR"].map(donortypes)
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return donortotals
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```
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