pandas drop_duplicate函数对于“唯一化”一个数据帧非常有用。我想删除在列的子集上重复的所有行。这可能吗?
A B C
0 foo 0 A
1 foo 1 A
2 foo 1 B
3 bar 1 A
例如,我想删除与列A和C匹配的行,因此这应该删除行0和1。
pandas drop_duplicate函数对于“唯一化”一个数据帧非常有用。我想删除在列的子集上重复的所有行。这可能吗?
A B C
0 foo 0 A
1 foo 1 A
2 foo 1 B
3 bar 1 A
例如,我想删除与列A和C匹配的行,因此这应该删除行0和1。
实际上,删除行0和1只需要(任何包含匹配的A和C的观测值都被保留):
In [335]:
df['AC']=df.A+df.C
In [336]:
print df.drop_duplicates('C', take_last=True) #this dataset is a special case, in general, one may need to first drop_duplicates by 'c' and then by 'a'.
A B C AC
2 foo 1 B fooB
3 bar 1 A barA
[2 rows x 4 columns]
但我怀疑你真正想要的是这个(一个包含匹配的A和C的观察结果被保留):
In [337]:
print df.drop_duplicates('AC')
A B C AC
0 foo 0 A fooA
2 foo 1 B fooB
3 bar 1 A barA
[3 rows x 4 columns]
编辑:
因此,现在情况清楚多了:
In [352]:
DG=df.groupby(['A', 'C'])
print pd.concat([DG.get_group(item) for item, value in DG.groups.items() if len(value)==1])
A B C
2 foo 1 B
3 bar 1 A
[2 rows x 3 columns]
使用groupby和filter
import pandas as pd
df = pd.DataFrame({"A":["foo", "foo", "foo", "bar"], "B":[0,1,1,1], "C":["A","A","B","A"]})
df.groupby(["A", "C"]).filter(lambda df:df.shape[0] == 1)
使用drop_duplicate和keep参数,这在pandas中要容易得多。
import pandas as pd
df = pd.DataFrame({"A":["foo", "foo", "foo", "bar"], "B":[0,1,1,1], "C":["A","A","B","A"]})
df.drop_duplicates(subset=['A', 'C'], keep=False)
只是想添加到Ben关于drop_duplicate的答案:
keep: {' first ', ' last ', False},默认' first '
first:删除除第一次出现之外的重复项。 last:删除除最后一次出现之外的重复项。 False:删除所有副本。
所以将keep设置为False会给你想要的答案。
DataFrame.drop_duplicates(*args, **kwargs) Return DataFrame with duplicate rows removed, optionally only considering certain columns Parameters: subset : column label or sequence of labels, optional Only consider certain columns for identifying duplicates, by default use all of the columns keep : {‘first’, ‘last’, False}, default ‘first’ first : Drop duplicates except for the first occurrence. last : Drop duplicates except for the last occurrence. False : Drop all duplicates. take_last : deprecated inplace : boolean, default False Whether to drop duplicates in place or to return a copy cols : kwargs only argument of subset [deprecated] Returns: deduplicated : DataFrame
如果你想将结果存储在另一个数据集中:
df.drop_duplicates(keep=False)
or
df.drop_duplicates(keep=False, inplace=False)
如果需要更新相同的数据集:
df.drop_duplicates(keep=False, inplace=True)
上面的例子将删除所有重复项并保留一个,类似于SQL中的DISTINCT *
试试这些不同的方法
df = pd.DataFrame({"A":["foo", "foo", "foo", "bar","foo"], "B":[0,1,1,1,1], "C":["A","A","B","A","A"]})
>>>df.drop_duplicates( "A" , keep='first')
or
>>>df.drop_duplicates( keep='first')
or
>>>df.drop_duplicates( keep='last')
如果你想用try和except语句检查两列,这个可以帮你。
if "column_2" in df.columns:
try:
df[['column_1', "column_2"]] = df[['header', "column_2"]].drop_duplicates(subset = ["column_2", "column_1"] ,keep="first")
except:
df[["column_2"]] = df[["column_2"]].drop_duplicates(subset="column_2" ,keep="first")
print(f"No column_1 for {path}.")
try:
df[["column_1"]] = df[["column_1"]].drop_duplicates(subset="column_1" ,keep="first")
except:
print(f"No column_1 or column_2 for {path}.")
您可以使用replicated()标记所有重复的行,并过滤掉标记的行。如果你以后需要将列赋值给new_df,确保调用.copy(),这样你以后就不会得到SettingWithCopyWarning。
new_df = df[~df.duplicated(subset=['A', 'C'], keep=False)].copy()
该方法的一个很好的特性是,您可以有条件地使用它删除重复项。例如,仅当列A等于'foo'时删除所有重复的行,您可以使用以下代码。
new_df = df[~( df.duplicated(subset=['A', 'B', 'C'], keep=False) & df['A'].eq('foo') )].copy()
此外,如果您不希望按名称写出列,您可以传递df的切片。列到子集=。对于drop_duplicate()也是如此。
# to consider all columns for identifying duplicates
df[~df.duplicated(subset=df.columns, keep=False)].copy()
# the same is true for drop_duplicates
df.drop_duplicates(subset=df.columns, keep=False)
# to consider columns in positions 0 and 2 (i.e. 'A' and 'C') for identifying duplicates
df.drop_duplicates(subset=df.columns[[0, 2]], keep=False)