1 files changed, 11 insertions, 43 deletions

diff --git a/fixup-perf-csv.py b/fixup-perf-csv.py
index 4e9e834..338dd33 100755
--- a/fixup-perf-csv.py
+++ b/fixup-perf-csv.py
@@ -7,55 +7,23 @@ import pandas as pd
 # (a) If dso is not present in perf.csv, it adds a dummy dso column.
 # (b) We don't have dso entry present for binary and libraries, so create a dummy one.
 # (c) Strip whitespace in dso name.
-# (d) Sets status field of symbol entries to 'na' and removes symbol entries
-#     for failed benchmarks.
 
 def main():
-    assert len(sys.argv) == 3
-    perf_df = pd.read_csv(sys.argv[1])
-    status_df = pd.read_csv(sys.argv[2])
-
-    if "dso" not in list(perf_df.columns.values):
-        perf_df["dso"] = "na"
-    perf_df = perf_df.fillna("na")
-    perf_df["dso"] = perf_df["dso"].str.strip()
-
-    merged_df = pd.merge(perf_df, status_df, how="outer", on=["benchmark", "symbol"])
-    # When all benchmarks have failed, perf-tmp.csv (perf_df) is empty.
-    # and this messes up order of columns while merging. Rearrange the columns
-    # to "expected order" with benchmark,symbol appearing first.
+    assert len(sys.argv) == 2
+    df = pd.read_csv(sys.argv[1])
+    if "dso" not in list(df.columns.values):
+        df["dso"] = "na"
+    df = df.fillna("na")
+    df["dso"] = df["dso"].str.strip()
+    # When all benchmarks have failed, perf.csv is empty, and this messes up
+    # order of columns while merging. Rearrange the columns to "expected order"
+    # with benchmark,symbol appearing first.
     # The order of columns shouldn't really be an issue, but we need
     # (or needed at some point) for benchmark to be the first metric, and thus
     # assert for it in merge-metric-csvs.py. This needs to be re-checked after
     # we move away from csvs2table.py.
-    merged_df = merged_df[["benchmark", "symbol", "sample", "dso", "status"]]
-
-    merged_df["sample"] = merged_df["sample"].fillna(-1).astype("int")
-    merged_df["dso"] = merged_df["dso"].fillna("na")
-    merged_df["status"] = merged_df["status"].fillna("na")
-
-    # FIXME: We end up having duplicates in status.csv for some runs.
-    # Remove duplicate entries from merged_df, if they occur in either perf-tmp.csv
-    # or status.csv.
-    merged_df = merged_df.drop_duplicates()
-
-    # Iterate over each row in merged_df, and only keep symbol
-    # entries for those bmks that ran successfully, so later metric and comparison
-    # scripts don't process symbols for failed benchmarks like LLVM-1070.
-    # FIXME: Growing a dataframe inside a loop can end up being terribly inefficient,
-    # revisit later.
-    res_df = pd.DataFrame(columns=merged_df.columns)
-    keep_symbol_row = True
-    for index, row in merged_df.iterrows():
-        if row['status'] != 'na' or keep_symbol_row:
-            res_df.loc[len(res_df)] = row
-            if row['status'] != 'na':
-                keep_symbol_row = True if row['status'] == 'success' \
-                                       else False
-
-    # Output perf.csv combined with status info and symbol entries removed
-    # for failed benchmarks.
-    res_df.to_csv(sys.stdout, index=False)
+    df = df[["benchmark", "symbol", "sample", "dso"]]
+    df.to_csv(sys.stdout, index=False)
 
 if __name__ == "__main__":
     main()