pandas “转置”一个熊猫系列
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"transpose" a Pandas Series
提问by lazy1
I have a DataFrame with an ID column and some features columns. I'd like to see a description of how many unique IDs are there per column values.
我有一个带有 ID 列和一些功能列的 DataFrame。我想查看每列值有多少个唯一 ID 的描述。
The following code works but I wonder if there a better way than the to_frame().unstack().unstack()line which transposes the .describe()series result to DataFrame where the columns are the percentiles, max, min ...
以下代码有效，但我想知道是否有比to_frame().unstack().unstack()将.describe()系列结果转置为 DataFrame的行更好的方法，其中列是百分位数、最大值、最小值......
def unique_ids(df):
rows = []
for col in sorted(c for c in df.columns if c != id_col):
v = df.groupby(col)[id_col].nunique().describe()
v = v.to_frame().unstack().unstack() # Transpose
v.index = [col]
rows.append(v)
return pd.concat(rows)
回答by jezrael
It seems you need change:
看来你需要改变：
v = v.to_frame().unstack().unstack()
to
到
v = v.to_frame().T
Or is possible transposefinal DataFrame, also is added renameby col:
或可能transpose最终DataFrame也加入rename的col：
df = pd.DataFrame({'ID':[1,1,3],
'E':[4,5,5],
'C':[7,8,9]})
print (df)
C E ID
0 7 4 1
1 8 5 1
2 9 5 3
def unique_ids(df):
rows = []
id_col = 'ID'
for col in sorted(c for c in df.columns if c != id_col):
v = df.groupby(col)[id_col].nunique().describe().rename(col)
rows.append(v)
return pd.concat(rows, axis=1).T
print (unique_ids(df))
count mean std min 25% 50% 75% max
C 3.0 1.0 0.000000 1.0 1.00 1.0 1.00 1.0
E 2.0 1.5 0.707107 1.0 1.25 1.5 1.75 2.0
