私のコードには、次のステートメントがあります。
df.loc[i] = [df.iloc[0][0], i, np.nan]
whereは、このステートメントが存在iするループで使用した反復変数であり、インポートした numpy モジュールであり、次のような DataFrame です。fornpdf
build_number name cycles
0 390 adpcm 21598
1 390 aes 5441
2 390 dfadd 463
3 390 dfdiv 1323
4 390 dfmul 167
5 390 dfsin 39589
6 390 gsm 6417
7 390 mips 4205
8 390 mpeg2 1993
9 390 sha 348417
ご覧のとおり、コード内のステートメントは、新しい行を DataFrame に挿入し、df(新しく挿入された行内の) 最後の列に値を入力cyclesしNaNます。
ただし、そうすると、次の警告メッセージが表示されます。
/usr/local/bin/ipython:28: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
ドキュメントを見ても、ここで発生している問題やリスクが何であるかをまだ理解していません。私はすでに推奨に従って使用locしていると思いましたか？iloc
ありがとうございました。
ここで編集 @EdChumのリクエストで、上記のステートメントを使用する関数を以下に追加しました。
def patch_missing_benchmarks(refined_dataframe):
'''
Patches up a given DataFrame, ensuring that all build_numbers have the complete
set of benchmark names, inserting NaN values at the column where the data is
supposed to be residing in.
Accepts:
--------
* refined_dataframe
DataFrame that was returned from the remove_early_retries() function and that
contains no duplicates of benchmarks within a given build number and also has been
sorted nicely to ensure that build numbers are in alphabetical order.
However, this function can also accept the DataFrame that has not been sorted, so
long as it has no repitition of benchmark names within a given build number.
Returns:
-------
* patched_benchmark_df
DataFrame with all Build numbers filled with the complete set of benchmark data,
with those previously missing benchmarks now having NaN values for their data.
'''
patched_df_list = []
benchmark_list = ['adpcm', 'aes', 'blowfish', 'dfadd', 'dfdiv', 'dfmul',
'dfsin', 'gsm', 'jpeg', 'mips', 'mpeg2', 'sha']
benchmark_series = pd.Series(data = benchmark_list)
for number in refined_dataframe['build_number'].drop_duplicates().values:
# df must be a DataFrame whose data has been sorted according to build_number
# followed by benchmark name
df = refined_dataframe.query('build_number == %d' % number)
# Now we compare the benchmark names present in our section of the DataFrame
# with the Series containing the complete collection of Benchmark names and
# get back a boolean DataFrame telling us precisely what benchmark names
# are missing
boolean_bench = benchmark_series.isin(df['name'])
list_names = []
for i in range(0, len(boolean_bench)):
if boolean_bench[i] == False:
name_to_insert = benchmark_series[i]
list_names.append(name_to_insert)
else:
continue
print 'These are the missing benchmarks for build number',number,':'
print list_names
for i in list_names:
# create a new row with index that is benchmark name itself to avoid overwriting
# any existing data, then insert the right values into that row, filling in the
# space name with the right benchmark name, and missing data with NaN
df.loc[i] = [df.iloc[0][0], i, np.nan]
patched_for_benchmarks_df = df.sort_index(by=['build_number',
'name']).reset_index(drop = True)
patched_df_list.append(patched_for_benchmarks_df)
# we make sure we call a dropna method at threshold 2 to drop those rows whose benchmark
# names as well as cycles names are NaN, leaving behind the newly inserted rows with
# benchmark names but that now have the data as NaN values
patched_benchmark_df = pd.concat(objs = patched_df_list, ignore_index =
True).sort_index(by= ['build_number',
'name']).dropna(thresh = 2).reset_index(drop = True)
return patched_benchmark_df