Python Pandas用户警告:正在排序，因为未串联的轴未对齐 [英] Python Pandas User Warning: Sorting because non-concatenation axis is not aligned
问题描述
我正在做一些代码练习，并在执行此操作时合并数据帧 收到用户警告
I'm doing some code practice and applying merging of data frames while doing this getting user warning
/usr/lib64/python2.7/site-packages/pandas/core/frame.py:6201:FutureWarning:排序是因为未串联的轴未对齐.未来版本 的熊猫将默认更改为不排序. 要接受将来的行为，请传递"sort = True". 要保留当前行为并消除警告，请传递sort = False
/usr/lib64/python2.7/site-packages/pandas/core/frame.py:6201: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version of pandas will change to not sort by default. To accept the future behavior, pass 'sort=True'. To retain the current behavior and silence the warning, pass sort=False
在这些代码行上:您能帮助解决此警告吗?
On these lines of code: Can you please help to get the solution of this warning.
placement_video = [self.read_sql_vdx_summary, self.read_sql_video_km]
placement_video_summary = reduce(lambda left, right: pd.merge(left, right, on='PLACEMENT', sort=False), placement_video)
placement_by_video = placement_video_summary.loc[:, ["PLACEMENT", "PLACEMENT_NAME", "COST_TYPE", "PRODUCT",
"VIDEONAME", "VIEW0", "VIEW25", "VIEW50", "VIEW75",
"VIEW100",
"ENG0", "ENG25", "ENG50", "ENG75", "ENG100", "DPE0",
"DPE25",
"DPE50", "DPE75", "DPE100"]]
# print (placement_by_video)
placement_by_video["Placement# Name"] = placement_by_video[["PLACEMENT",
"PLACEMENT_NAME"]].apply(lambda x: ".".join(x),
axis=1)
placement_by_video_new = placement_by_video.loc[:,
["PLACEMENT", "Placement# Name", "COST_TYPE", "PRODUCT", "VIDEONAME",
"VIEW0", "VIEW25", "VIEW50", "VIEW75", "VIEW100",
"ENG0", "ENG25", "ENG50", "ENG75", "ENG100", "DPE0", "DPE25",
"DPE50", "DPE75", "DPE100"]]
placement_by_km_video = [placement_by_video_new, self.read_sql_km_for_video]
placement_by_km_video_summary = reduce(lambda left, right: pd.merge(left, right, on=['PLACEMENT', 'PRODUCT'], sort=False),
placement_by_km_video)
#print (list(placement_by_km_video_summary))
#print(placement_by_km_video_summary)
#exit()
# print(placement_by_video_new)
"""Conditions for 25%view"""
mask17 = placement_by_km_video_summary["PRODUCT"].isin(['Display', 'Mobile'])
mask18 = placement_by_km_video_summary["COST_TYPE"].isin(["CPE", "CPM", "CPCV"])
mask19 = placement_by_km_video_summary["PRODUCT"].isin(["InStream"])
mask20 = placement_by_km_video_summary["COST_TYPE"].isin(["CPE", "CPM", "CPE+", "CPCV"])
mask_video_video_completions = placement_by_km_video_summary["COST_TYPE"].isin(["CPCV"])
mask21 = placement_by_km_video_summary["COST_TYPE"].isin(["CPE+"])
mask22 = placement_by_km_video_summary["COST_TYPE"].isin(["CPE", "CPM"])
mask23 = placement_by_km_video_summary["PRODUCT"].isin(['Display', 'Mobile', 'InStream'])
mask24 = placement_by_km_video_summary["COST_TYPE"].isin(["CPE", "CPM", "CPE+"])
choice25video_eng = placement_by_km_video_summary["ENG25"]
choice25video_vwr = placement_by_km_video_summary["VIEW25"]
choice25video_deep = placement_by_km_video_summary["DPE25"]
placement_by_km_video_summary["25_pc_video"] = np.select([mask17 & mask18, mask19 & mask20, mask17 & mask21],
[choice25video_eng, choice25video_vwr, choice25video_deep])
"""Conditions for 50%view"""
choice50video_eng = placement_by_km_video_summary["ENG50"]
choice50video_vwr = placement_by_km_video_summary["VIEW50"]
choice50video_deep = placement_by_km_video_summary["DPE50"]
placement_by_km_video_summary["50_pc_video"] = np.select([mask17 & mask18, mask19 & mask20, mask17 & mask21],
[choice50video_eng,
choice50video_vwr, choice50video_deep])
"""Conditions for 75%view"""
choice75video_eng = placement_by_km_video_summary["ENG75"]
choice75video_vwr = placement_by_km_video_summary["VIEW75"]
choice75video_deep = placement_by_km_video_summary["DPE75"]
placement_by_km_video_summary["75_pc_video"] = np.select([mask17 & mask18, mask19 & mask20, mask17 & mask21],
[choice75video_eng,
choice75video_vwr,
choice75video_deep])
"""Conditions for 100%view"""
choice100video_eng = placement_by_km_video_summary["ENG100"]
choice100video_vwr = placement_by_km_video_summary["VIEW100"]
choice100video_deep = placement_by_km_video_summary["DPE100"]
choicecompletions = placement_by_km_video_summary['COMPLETIONS']
placement_by_km_video_summary["100_pc_video"] = np.select([mask17 & mask22, mask19 & mask24, mask17 & mask21, mask23 & mask_video_video_completions],
[choice100video_eng, choice100video_vwr, choice100video_deep, choicecompletions])
"""conditions for 0%view"""
choice0video_eng = placement_by_km_video_summary["ENG0"]
choice0video_vwr = placement_by_km_video_summary["VIEW0"]
choice0video_deep = placement_by_km_video_summary["DPE0"]
placement_by_km_video_summary["Views"] = np.select([mask17 & mask18, mask19 & mask20, mask17 & mask21],
[choice0video_eng,
choice0video_vwr,
choice0video_deep])
#print (placement_by_km_video_summary)
#exit()
#final Table
placement_by_video_summary = placement_by_km_video_summary.loc[:,
["PLACEMENT", "Placement# Name", "PRODUCT", "VIDEONAME", "COST_TYPE",
"Views", "25_pc_video", "50_pc_video", "75_pc_video","100_pc_video",
"ENGAGEMENTS","IMPRESSIONS", "DPEENGAMENTS"]]
#placement_by_km_video = [placement_by_video_summary, self.read_sql_km_for_video]
#placement_by_km_video_summary = reduce(lambda left, right: pd.merge(left, right, on=['PLACEMENT', 'PRODUCT']),
#placement_by_km_video)
#print(placement_by_video_summary)
#exit()
# dup_col =["IMPRESSIONS","ENGAGEMENTS","DPEENGAMENTS"]
# placement_by_video_summary.loc[placement_by_video_summary.duplicated(dup_col),dup_col] = np.nan
# print ("Dhar",placement_by_video_summary)
'''adding views based on conditions'''
#filter maximum value from videos
placement_by_video_summary_new = placement_by_km_video_summary.loc[
placement_by_km_video_summary.reset_index().groupby(['PLACEMENT', 'PRODUCT'])['Views'].idxmax()]
#print (placement_by_video_summary_new)
#exit()
# print (placement_by_video_summary_new)
# mask22 = (placement_by_video_summary_new.PRODUCT.str.upper ()=='DISPLAY') & (placement_by_video_summary_new.COST_TYPE=='CPE')
placement_by_video_summary_new.loc[mask17 & mask18, 'Views'] = placement_by_video_summary_new['ENGAGEMENTS']
placement_by_video_summary_new.loc[mask19 & mask20, 'Views'] = placement_by_video_summary_new['IMPRESSIONS']
placement_by_video_summary_new.loc[mask17 & mask21, 'Views'] = placement_by_video_summary_new['DPEENGAMENTS']
#print (placement_by_video_summary_new)
#exit()
placement_by_video_summary = placement_by_video_summary.drop(placement_by_video_summary_new.index).append(
placement_by_video_summary_new).sort_index()
placement_by_video_summary["Video Completion Rate"] = placement_by_video_summary["100_pc_video"] / \
placement_by_video_summary["Views"]
placement_by_video_final = placement_by_video_summary.loc[:,
["Placement# Name", "PRODUCT", "VIDEONAME", "Views",
"25_pc_video", "50_pc_video", "75_pc_video", "100_pc_video",
"Video Completion Rate"]]
推荐答案
tl; dr:
concat和append当前对非连接索引(例如，如果要添加行的列)进行排序.在大熊猫0.23中，这开始产生警告.传递参数sort=True使其静音.将来，默认值将更改为不排序，因此最好现在指定sort=True或False，或者最好确保您的非串联索引匹配.
concat and append currently sort the non-concatenation index (e.g. columns if you're adding rows) if the columns don't match. In pandas 0.23 this started generating a warning; pass the parameter sort=True to silence it. In the future the default will change to not sort, so it's best to specify either sort=True or False now, or better yet ensure that your non-concatenation indices match.
该警告是 pandas 0.23.0 中的新内容:
在将来的熊猫版本中，pandas.concat()和DataFrame.append()将不再对未串联的轴进行排序.当前行为与先前的行为相同(排序)，但是现在当未指定排序且未连接的轴未对齐时会发出警告，
链接
In a future version of pandas pandas.concat() and DataFrame.append() will no longer sort the non-concatenation axis when it is not already aligned. The current behavior is the same as the previous (sorting), but now a warning is issued when sort is not specified and the non-concatenation axis is not aligned,
link.
链接的非常古老的 github问题，由smcinerney发表评论:
在连接DataFrame时，如果列名称之间存在任何差异，则列名称将按字母数字顺序进行排序.如果它们在DataFrame中是相同的，则不会被排序.
When concat'ing DataFrames, the column names get alphanumerically sorted if there are any differences between them. If they're identical across DataFrames, they don't get sorted.
这种记录是无证的，不需要的.当然，默认行为应该是不排序.
This sort is undocumented and unwanted. Certainly the default behavior should be no-sort.
一段时间后，参数sort在 pandas.concat 和
After some time the parameter sort was implemented in pandas.concat and DataFrame.append:
排序:布尔值，默认为无
sort : boolean, default None
如果连接为外部"时未对齐轴，则对它进行排序.当前默认的排序默认值已弃用，在以后的熊猫版本中将更改为不排序".
Sort non-concatenation axis if it is not already aligned when join is 'outer'. The current default of sorting is deprecated and will change to not-sorting in a future version of pandas.
显式传递sort = True可以使警告和排序保持静音.显式传递sort = False可以使警告静音而不进行排序.
Explicitly pass sort=True to silence the warning and sort. Explicitly pass sort=False to silence the warning and not sort.
当join ='inner'时，此命令无效，该命令已经保留了非串联轴的顺序.
This has no effect when join='inner', which already preserves the order of the non-concatenation axis.
因此，如果两个DataFrame都以相同的顺序具有相同的列，则不会发出警告，也不会进行排序:
So if both DataFrames have the same columns in the same order, there is no warning and no sorting:
df1 = pd.DataFrame({"a": [1, 2], "b": [0, 8]}, columns=['a', 'b'])
df2 = pd.DataFrame({"a": [4, 5], "b": [7, 3]}, columns=['a', 'b'])
print (pd.concat([df1, df2]))
a b
0 1 0
1 2 8
0 4 7
1 5 3
df1 = pd.DataFrame({"a": [1, 2], "b": [0, 8]}, columns=['b', 'a'])
df2 = pd.DataFrame({"a": [4, 5], "b": [7, 3]}, columns=['b', 'a'])
print (pd.concat([df1, df2]))
b a
0 0 1
1 8 2
0 7 4
1 3 5
但是，如果数据帧具有不同的列或相同的列，但顺序不同，则如果未显式设置参数sort(sort=None是默认值)，pandas将返回警告:
But if the DataFrames have different columns, or the same columns in a different order, pandas returns a warning if no parameter sort is explicitly set (sort=None is the default value):
df1 = pd.DataFrame({"a": [1, 2], "b": [0, 8]}, columns=['b', 'a'])
df2 = pd.DataFrame({"a": [4, 5], "b": [7, 3]}, columns=['a', 'b'])
print (pd.concat([df1, df2]))
FutureWarning:排序是因为未连接的轴未对齐.
FutureWarning: Sorting because non-concatenation axis is not aligned.
a b
0 1 0
1 2 8
0 4 7
1 5 3
print (pd.concat([df1, df2], sort=True))
a b
0 1 0
1 2 8
0 4 7
1 5 3
print (pd.concat([df1, df2], sort=False))
b a
0 0 1
1 8 2
0 7 4
1 3 5
如果DataFrames的列不同，但前几列对齐-它们将彼此正确分配(df1中的列a和b以及df2中的a和b在下面的示例中)，因为它们同时存在于两者中.对于在一个但不是两个DataFrame中都存在的其他列，将创建缺少的值.
If the DataFrames have different columns, but the first columns are aligned - they will be correctly assigned to each other (columns a and b from df1 with a and b from df2 in the example below) because they exist in both. For other columns that exist in one but not both DataFrames, missing values are created.
最后，如果您通过sort=True，则列将按字母数字顺序进行排序.如果sort=False和第二个DafaFrame的列不在第一列中，则它们将不进行排序地附加到末尾:
Lastly, if you pass sort=True, columns are sorted alphanumerically. If sort=False and the second DafaFrame has columns that are not in the first, they are appended to the end with no sorting:
df1 = pd.DataFrame({"a": [1, 2], "b": [0, 8], 'e':[5, 0]},
columns=['b', 'a','e'])
df2 = pd.DataFrame({"a": [4, 5], "b": [7, 3], 'c':[2, 8], 'd':[7, 0]},
columns=['c','b','a','d'])
print (pd.concat([df1, df2]))
FutureWarning:排序是因为未连接的轴未对齐.
FutureWarning: Sorting because non-concatenation axis is not aligned.
a b c d e
0 1 0 NaN NaN 5.0
1 2 8 NaN NaN 0.0
0 4 7 2.0 7.0 NaN
1 5 3 8.0 0.0 NaN
print (pd.concat([df1, df2], sort=True))
a b c d e
0 1 0 NaN NaN 5.0
1 2 8 NaN NaN 0.0
0 4 7 2.0 7.0 NaN
1 5 3 8.0 0.0 NaN
print (pd.concat([df1, df2], sort=False))
b a e c d
0 0 1 5.0 NaN NaN
1 8 2 0.0 NaN NaN
0 7 4 NaN 2.0 7.0
1 3 5 NaN 8.0 0.0
在您的代码中:
placement_by_video_summary = placement_by_video_summary.drop(placement_by_video_summary_new.index)
.append(placement_by_video_summary_new, sort=True)
.sort_index()
这篇关于Python Pandas用户警告:正在排序，因为未串联的轴未对齐的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
