.

gis/CV_coastal.py

@@ -307,7 +307,7 @@ def stepwise_selection(subset, dummies, data, dfDummies, years):
     a.to_csv("output/coastal_" + b + ".csv")  #  save means by subset to CSV
     f = {row: "{:0.0f}".format if row == "n" else "{:0.4f}".format for row in a.index}
     with open("output/coastal_" + b + ".tex", "w") as tf:
-        tf.write(a.apply(f, axis=1).to_latex())  #  apply formatter and save to LaTeX
+        tf.write(a.apply(f, axis=1).to_latex(column_format="lcc"))  #  column alignment
 
 
 ########################################################################################

gis/CV_lakes.py

@@ -300,7 +300,7 @@ def stepwise_selection(subset, dummies, data, dfDummies, years):
     a.to_csv("output/lakes_" + b + ".csv")  #  save means by subset to CSV
     f = {row: "{:0.0f}".format if row == "n" else "{:0.4f}".format for row in a.index}
     with open("output/lakes_" + b + ".tex", "w") as tf:
-        tf.write(a.apply(f, axis=1).to_latex())  #  apply formatter and save to LaTeX
+        tf.write(a.apply(f, axis=1).to_latex(column_format="lccc"))  #  column alignment
 
 
 ########################################################################################

gis/CV_streams.py

@@ -323,7 +323,7 @@ def stepwise_selection(subset, dummies, data, dfDummies, years, select_all=False
     a.to_csv("output/streams_" + b + ".csv")  #  save means by subset to CSV
     f = {row: "{:0.0f}".format if row == "n" else "{:0.4f}".format for row in a.index}
     with open("output/streams_" + b + ".tex", "w") as tf:
-        tf.write(a.apply(f, axis=1).to_latex())  #  apply formatter and save to LaTeX
+        tf.write(a.apply(f, axis=1).to_latex(column_format="lccc"))  #  column alignment
 
 
 ########################################################################################

gis/output/all_cost_decomposed_growth.tex

@@ -1,4 +1,4 @@
-\begin{tabular}{llll}
+\begin{tabular}{lrrr}
 \toprule
  & growth (million DKK) & growth (\%) & growth rate (\%) \\
 driver &  &  &  \\

gis/output/all_cost_decomposed_growth_v.tex

@@ -1,4 +1,4 @@
-\begin{tabular}{llllllll}
+\begin{tabular}{lrrrrrrr}
 \toprule
  & mean & std & min & 25\% & 50\% & 75\% & max \\
 driver &  &  &  &  &  &  &  \\

gis/output/all_investment_decomposed_v.tex

@@ -1,4 +1,4 @@
-\begin{tabular}{llllllll}
+\begin{tabular}{lrrrrrrr}
 \toprule
  & mean & std & min & 25\% & 50\% & 75\% & max \\
 driver &  &  &  &  &  &  &  \\
@@ -7,5 +7,5 @@
 lakes & 2,533 & 1,619 & -2,161 & 1,577 & 2,367 & 3,390 & 7,059 \\
 streams & 4,843 & 4,501 & -7,313 & 2,091 & 5,522 & 7,959 & 17,402 \\
 all & 8,062 & 4,375 & -4,551 & 5,024 & 8,003 & 10,503 & 21,378 \\
-\bottomrule
+\bottomrules
 \end{tabular}

gis/output/coastal_VP_basis.csv

@@ -12,5 +12,4 @@ Stratified,1.605263157894737,1.7333333333333334
 Sediment,1.6785714285714286,1.6774193548387097
 Saline,1.6,1.7435897435897436
 Tide,1.4285714285714286,1.4285714285714286
-All,1.6458333333333333,1.712962962962963
-n,96.0,108.0
+All,96.0,108.0

gis/output/coastal_VP_basis.tex

@@ -1,4 +1,4 @@
-\begin{tabular}{lll}
+\begin{tabular}{lcc}
 \toprule
  & Observed subset & All in basis analysis \\
 \midrule
@@ -15,7 +15,6 @@
 Sediment & 1.6786 & 1.6774 \\
 Saline & 1.6000 & 1.7436 \\
 Tide & 1.4286 & 1.4286 \\
-All & 1.6458 & 1.7130 \\
-n & 96 & 108 \\
+All & 96.0000 & 108.0000 \\
 \bottomrule
 \end{tabular}

gis/output/coastal_VP_stats.tex

@@ -1,4 +1,4 @@
-\begin{tabular}{lll}
+\begin{tabular}{lcc}
 \toprule
  & Observed subset & All in VP3 \\
 \midrule

gis/output/lakes_VP_basis.tex

@@ -1,4 +1,4 @@
-\begin{tabular}{llll}
+\begin{tabular}{lccc}
 \toprule
  & Sparse subset & Observed subset & All in basis analysis \\
 \midrule

gis/output/lakes_VP_stats.tex

@@ -1,4 +1,4 @@
-\begin{tabular}{llll}
+\begin{tabular}{lccc}
 \toprule
  & Sparse subset & Observed subset & All in VP3 \\
 \midrule

gis/output/streams_VP_basis.tex

@@ -1,4 +1,4 @@
-\begin{tabular}{llll}
+\begin{tabular}{lccc}
 \toprule
  & Sparse subset & Observed subset & All in basis analysis \\
 \midrule

gis/output/streams_VP_stats.tex

@@ -1,4 +1,4 @@
-\begin{tabular}{llll}
+\begin{tabular}{lccc}
 \toprule
  & Sparse subset & Observed subset & All in VP3 \\
 \midrule

gis/sandbox.py

@@ -306,6 +306,7 @@
             col: "{:0,.0f}".format if col == growth.columns[0] else "{:0.2f}".format
             for col in growth.columns
         }
+        col_f = "lrrr"  #  right-aligned column format; match number of columns
         print("Growth in total CWP due to driver (other things equal at 2018 level)")
         print(d.tail(3), "\n")
     else:
@@ -319,13 +320,14 @@
             )
         growth = d[d.index.get_level_values("t") == "g (%)"].describe().drop("count").T
         growth.columns = ["mean", "std", "min", "25\%", "50\%", "75\%", "max"]
-        f = {col: "{:0.2f}".format for col in growth.columns}
+        f = {col: "{:0.2f}".format for col in growth.columns}  #  two decimals
+        col_f = "lrrrrrrr"  #  right-aligned column format; match number of columns
         print("The 15 catchment areas with largest reduction in total CWP")
         print(d.loc["g (%)", :].nsmallest(15, ("all")), "\n")
         print("The 15 catchment areas with largest increase in total CWP")
         print(d.loc["g (%)", :].nlargest(15, ("all")), "\n")
     with open("output\\all_cost_decomposed_growth" + suffix + ".tex", "w") as tf:
-        tf.write(growth.apply(f).to_latex())  #  apply formatter and save to LaTeX
+        tf.write(growth.apply(f).to_latex(column_format=col_f))  #  column alignment
     d.to_csv("output\\all_cost_decomposed" + suffix + ".csv")  #  save table as CSV
 
 # Colors and line styles by category j - matching those used for total CWP decomposition
@@ -382,7 +384,7 @@
         mean.columns = ["mean", "std", "min", "25\%", "50\%", "75\%", "max"]
         f = {col: "{:0,.0f}".format for col in mean.columns}
         with open("output\\all_investment_decomposed_v.tex", "w") as tf:
-            tf.write(mean.apply(f).to_latex())  #  apply formatter and save to LaTeX
+            tf.write(mean.apply(f).to_latex(column_format="lrrrrrrr"))  #  col alignment
         print("The 15 catchment areas with lowest total IV per household")
         print(d.loc["mean IV", :].nsmallest(15, ("all")), "\n")
         print("The 15 catchment areas with highest total IV per household")

gis/script.py

@@ -270,6 +270,7 @@
             col: "{:0,.0f}".format if col == growth.columns[0] else "{:0.2f}".format
             for col in growth.columns
         }
+        col_f = "lrrr"  #  right-aligned column format; match number of columns
         print("Growth in total CWP due to driver (other things equal at 2018 level)")
         print(d.tail(3), "\n")
     else:
@@ -283,13 +284,14 @@
             )
         growth = d[d.index.get_level_values("t") == "g (%)"].describe().drop("count").T
         growth.columns = ["mean", "std", "min", "25\%", "50\%", "75\%", "max"]
-        f = {col: "{:0.2f}".format for col in growth.columns}
+        f = {col: "{:0.2f}".format for col in growth.columns}  #  two decimals
+        col_f = "lrrrrrrr"  #  right-aligned column format; match number of columns
         print("The 15 catchment areas with largest reduction in total CWP")
         print(d.loc["g (%)", :].nsmallest(15, ("all")), "\n")
         print("The 15 catchment areas with largest increase in total CWP")
         print(d.loc["g (%)", :].nlargest(15, ("all")), "\n")
     with open("output\\all_cost_decomposed_growth" + suffix + ".tex", "w") as tf:
-        tf.write(growth.apply(f).to_latex())  #  apply formatter and save to LaTeX
+        tf.write(growth.apply(f).to_latex(column_format=col_f))  #  column alignment
     d.to_csv("output\\all_cost_decomposed" + suffix + ".csv")  #  save table as CSV
 
 # Colors and line styles by category j - matching those used for total CWP decomposition
@@ -346,7 +348,7 @@
         mean.columns = ["mean", "std", "min", "25\%", "50\%", "75\%", "max"]
         f = {col: "{:0,.0f}".format for col in mean.columns}
         with open("output\\all_investment_decomposed_v.tex", "w") as tf:
-            tf.write(mean.apply(f).to_latex())  #  apply formatter and save to LaTeX
+            tf.write(mean.apply(f).to_latex(column_format="lrrrrrrr"))  #  col alignment
         print("The 15 catchment areas with lowest total IV per household")
         print(d.loc["mean IV", :].nsmallest(15, ("all")), "\n")
         print("The 15 catchment areas with highest total IV per household")