我正在尝试删除 pandas DataFrame 中的一些观察结果,其中相似性几乎为 100%,但不完全一致.见下图:
I am attempting to remove some observations in a pandas DataFrame where the similarities are ALMOST 100% but not quite. See frame below:
注意John"、Mary"和Wesley"是如何出现的具有几乎相同的观察结果,但有一列不同.真实数据集有 15 列和 215,000 多个观测值.在我可以直观验证的所有情况下,相似之处同样是:在 15 列中,其他观察每次最多匹配 14 列.为了项目的目的,我决定删除重复的观察结果(并将它们存储到另一个 DataFrame 中,以防我的老板要求查看它们).
Notice how "John", "Mary", and "Wesley" have nearly identical observations, but have one column being different. The real data set has 15 columns, and 215,000+ observations. In all of the cases I could visually verify, the similarities were likewise: out of 15 columns, the other observation would match up to 14 columns, every time. For the purpose of the project I have decided to remove the repeated observations (and store them into another DataFrame just in case my boss asks to see them).
我显然已经想到了 remove_duplicates(keep='something')
,但这行不通,因为观察结果并不完全相似.有没有人遇到过这样的问题?有什么补救办法吗?
I have evidently thought of remove_duplicates(keep='something')
, but that would not work since the observations are not ENTIRELY similar. Has anyone ever encounter such an issue? Any idea on a remedy?
关于列子集的简单循环怎么样:
What about a simple loop over subset of columns :
import pandas as pd
df = pd.DataFrame(
[
['John', 45, 85000, 'DC'],
['Netcha', 25, 48000, 'NYC'],
['Mary', 45, 85000, 'DC'],
['Wesley', 36, 72500, 'LA'],
['Porter', 22, 98750, 'Seattle'],
['John', 45, 105500, 'DC'],
['Mary', 28, 85000, 'DC'],
['Wesley', 36, 72500, 'Boston'],
],
columns=['Name', 'Age', 'Salary', 'City'])
cols = df.columns.tolist()
cols.remove('Name')
for col in cols:
observed_cols = df.drop(col, axis=1).columns.tolist()
df.drop_duplicates(observed_cols, keep='first', inplace=True)
print(df)
返回:
Name Age Salary City
0 John 45 85000 DC
1 Netcha 25 48000 NYC
2 Mary 45 85000 DC
3 Wesley 36 72500 LA
4 Porter 22 98750 Seattle
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