Build total donator type visualization for Benin
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@ -100,3 +100,25 @@ The primary reasons for not having access to electricity are the lack of physica
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as well as connection costs to the main electrical grid being too high.
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To decrease the effects of this driving force of inequality,
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both infrastructural expansion as well as policy commitments toward affordable connections to electrical grids are thus of vital importance.
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<!-- development assistance -->
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```{python}
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#| label: fig-ben-aid-donortype
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#| fig-cap: "Total ODA for Benin per year, separated by donor type. Source: "
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#| column: page
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dfsub1 = pd.read_csv('data/raw/OECD_CRS/CRS1_Benin_11-13_05092022185506030.csv', parse_dates=True, low_memory=False)
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dfsub2 = pd.read_csv('data/raw/OECD_CRS/CRS1_Benin_14-16_05092022192438936.csv', parse_dates=True, low_memory=False)
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dfsub3 = pd.read_csv('data/raw/OECD_CRS/CRS1_Benin_17-20_05092022192856890.csv', parse_dates=True, low_memory=False)
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df = pd.concat([dfsub1, dfsub2, dfsub3], ignore_index=True)
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df = df.rename(columns={'\ufeff"DONOR"': 'DONOR'})
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donortotals = totals_by_donortype(df)
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donortotals_grouped = donortotals.groupby(['Donortype', 'Year']).agg({'Value': ['sum']})
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donortotals_grouped = donortotals_grouped.reset_index(['Donortype', 'Year'])
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donortotals_grouped.columns = donortotals_grouped.columns.to_flat_index()
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donortotals_grouped.columns = ['Donortype', 'Year', 'Value']
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fig = px.line(donortotals_grouped, x='Year', y='Value', color='Donortype', labels={"Value": "Development aid, in millions"}, markers=True, template="seaborn")
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fig.show()
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```
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179
data-prep.qmd
179
data-prep.qmd
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@ -61,3 +61,182 @@ def plot_consumption_gini_percapita_ruralurban(country_df):
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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: 'multilat',
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807: 'multilat',
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811: 'multilat',
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812: 'multilat',
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901: 'multilat',
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902: 'multilat',
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903: 'multilat',
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905: 'multilat',
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906: 'multilat',
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907: 'multilat',
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909: 'multilat',
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913: 'multilat',
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914: 'multilat',
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915: 'multilat',
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921: 'multilat',
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923: 'multilat',
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926: 'multilat',
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928: 'multilat',
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932: 'multilat',
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940: 'multilat',
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944: 'multilat',
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948: 'multilat',
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951: 'multilat',
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952: 'multilat',
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953: 'multilat',
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954: 'multilat',
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956: 'multilat',
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958: 'multilat',
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959: 'multilat',
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960: 'multilat',
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963: 'multilat',
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964: 'multilat',
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966: 'multilat',
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967: 'multilat',
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971: 'multilat',
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974: 'multilat',
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976: 'multilat',
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978: 'multilat',
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979: 'multilat',
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980: 'multilat',
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981: 'multilat',
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982: 'multilat',
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983: 'multilat',
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988: 'multilat',
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990: 'multilat',
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992: 'multilat',
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997: 'multilat',
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1011: 'multilat',
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1012: 'multilat',
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1013: 'multilat',
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1014: 'multilat',
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1015: 'multilat',
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1016: 'multilat',
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1017: 'multilat',
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1018: 'multilat',
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1019: 'multilat',
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1020: 'multilat',
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1023: 'multilat',
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1024: 'multilat',
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1025: 'multilat',
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1037: 'multilat',
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1038: 'multilat',
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1311: 'multilat',
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1312: 'multilat',
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1313: 'multilat',
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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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