pandas 如何在熊猫多索引数据框中仅选择索引列？
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How to select ONLY THE INDEX COLUMNS in a pandas multi-index Dataframe?
提问by Archan Joshi
Okay, so I have a DataFrame with a 2 column index, and I am trying to filter the rows from that DataFrame and keep ONLY THE INDEX COLUMNS of the original dataframe into the new filtered DataFrame.
好的，所以我有一个带有 2 列索引的 DataFrame，我正在尝试过滤该 DataFrame 中的行，并仅将原始数据帧的 INDEX COLUMNS 保留到新过滤的 DataFrame 中。
I created the dataframe from a CSV file by: Find the CSV file here
我通过以下方式从 CSV 文件创建了数据框：在此处查找 CSV 文件
census_df = pd.read_csv("census.csv", index_col = ["STNAME", "CTYNAME"])
census_df.sort_index(ascending = True)
Then, I applied some filtering to the DataFrame, which works perfectly fine, and I get the desired rows. The code I used is shown below:
然后，我对 DataFrame 应用了一些过滤，效果很好，我得到了所需的行。我使用的代码如下所示：
def my_answer():
mask1 = census_df["REGION"].between(1, 2)
mask2 = census_df.index.get_level_values("CTYNAME").str.startswith("Washington")
mask3 = (census_df["POPESTIMATE2015"] > census_df["POPESTIMATE2014"])
new_df = census_df[mask1 & mask2 & mask3]
return pd.DataFrame(new_df.iloc[:, -1])
my_answer()
Here is the problem:
这是问题所在：
The above code returns a dataframe with the index AND the first column IN ADDITION to the 2 index columns. What I want is JUST THE TWO INDEX COLUMNS. So, the final answer should return a DATAFRAME, with "STNAME" and "CTYNAME", with 5 rows in it.
上面的代码返回一个带有索引和第一列 IN ADDITION 到 2 个索引列的数据框。我想要的只是两个索引列。因此，最终答案应该返回一个 DATAFRAME，带有“STNAME”和“CTYNAME”，其中有 5 行。
采纳答案by jezrael
You can convert indexto DataFrame:
您可以转换index为DataFrame：
def my_answer():
mask1 = census_df["REGION"].between(1, 2)
mask2 = census_df.index.get_level_values("CTYNAME").str.startswith("Washington")
mask3 = (census_df["POPESTIMATE2015"] > census_df["POPESTIMATE2014"])
new_df = census_df[mask1 & mask2 & mask3]
return pd.DataFrame(new_df.index.tolist(), columns=['STNAME','CTYNAME'])
print (my_answer())
STNAME CTYNAME
0 Iowa Washington County
1 Minnesota Washington County
2 Pennsylvania Washington County
3 Rhode Island Washington County
4 Wisconsin Washington County
If want output as MultiIndexneed MultiIndex.remove_unused_levels, but it working in pandas 0.20.0+:
如果需要根据MultiIndex需要输出MultiIndex.remove_unused_levels，但它可以在pandas 0.20.0+：
def my_answer():
mask1 = census_df["REGION"].between(1, 2)
mask2 = census_df.index.get_level_values("CTYNAME").str.startswith("Washington")
mask3 = (census_df["POPESTIMATE2015"] > census_df["POPESTIMATE2014"])
new_df = census_df[mask1 & mask2 & mask3]
return new_df.index.remove_unused_levels()
print (my_answer())
MultiIndex(levels=[['Iowa', 'Minnesota', 'Pennsylvania', 'Rhode Island', 'Wisconsin'],
['Washington County']],
labels=[[0, 1, 2, 3, 4], [0, 0, 0, 0, 0]],
names=['STNAME', 'CTYNAME'])
回答by Hermes Morales
Using list comprehension:
使用列表理解：
def my_answer():
mask1 = census_df["REGION"].between(1, 2)
mask2 = census_df.index.get_level_values("CTYNAME").str.startswith("Washington")
mask3 = (census_df["POPESTIMATE2015"] > census_df["POPESTIMATE2014"])
new_df = census_df[mask1 & mask2 & mask3]
return pd.DataFrame([new_df.index[x] for x in range(len(new_df))])
my_answer()
Output:
输出：
0 1
0 Iowa Washington County
1 Minnesota Washington County
2 Pennsylvania Washington County
3 Rhode Island Washington County
4 Wisconsin Washington County``
