Python 将新行添加到具有特定索引名称的 Pandas DataFrame
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Add a new row to a Pandas DataFrame with specific index name
提问by samba
I'm trying to add a new row to the DataFrame with a specific index name 'e'.
我正在尝试向具有特定索引名称的 DataFrame 添加新行'e'。
number variable values
a NaN bank true
b 3.0 shop false
c 0.5 market true
d NaN government true
I have tried the following but it's creating a new column instead of a new row.
我尝试了以下操作，但它正在创建一个新列而不是新行。
new_row = [1.0, 'hotel', 'true']
df = df.append(new_row)
Still don't understand how to insert the row with a specific index. Will be grateful for any suggestions.
仍然不明白如何插入具有特定索引的行。将不胜感激任何建议。
回答by MaxU
You can use df.loc[_not_yet_existing_index_label_] = new_row.
您可以使用df.loc[_not_yet_existing_index_label_] = new_row.
Demo:
演示：
In [3]: df.loc['e'] = [1.0, 'hotel', 'true']
In [4]: df
Out[4]:
number variable values
a NaN bank True
b 3.0 shop False
c 0.5 market True
d NaN government True
e 1.0 hotel true
PS using this method you can't add a row with already existing (duplicate) index value (label) - a row with this index label will be updatedin this case.
PS 使用此方法您无法添加具有现有（重复）索引值（标签）的行 -在这种情况下将更新具有此索引标签的行。
UPDATE:
更新：
This might not work in recent Pandas/Python3 if the index is a DateTimeIndex and the new row's index doesn't exist.
如果索引是 DateTimeIndex 并且新行的索引不存在，这在最近的 Pandas/Python3 中可能不起作用。
it'll work if we specify correct index value(s).
如果我们指定正确的索引值，它将起作用。
Demo (using pandas: 0.23.4):
演示（使用pandas: 0.23.4）：
In [17]: ix = pd.date_range('2018-11-10 00:00:00', periods=4, freq='30min')
In [18]: df = pd.DataFrame(np.random.randint(100, size=(4,3)), columns=list('abc'), index=ix)
In [19]: df
Out[19]:
a b c
2018-11-10 00:00:00 77 64 90
2018-11-10 00:30:00 9 39 26
2018-11-10 01:00:00 63 93 72
2018-11-10 01:30:00 59 75 37
In [20]: df.loc[pd.to_datetime('2018-11-10 02:00:00')] = [100,100,100]
In [21]: df
Out[21]:
a b c
2018-11-10 00:00:00 77 64 90
2018-11-10 00:30:00 9 39 26
2018-11-10 01:00:00 63 93 72
2018-11-10 01:30:00 59 75 37
2018-11-10 02:00:00 100 100 100
In [22]: df.index
Out[22]: DatetimeIndex(['2018-11-10 00:00:00', '2018-11-10 00:30:00', '2018-11-10 01:00:00', '2018-11-10 01:30:00', '2018-11-10 02:00:00'], dtype='da
tetime64[ns]', freq=None)
回答by Bharath
Use append by converting list a dataframe in case you want to add multiple rows at once i.e
如果您想一次添加多行，请通过转换列表数据帧来使用附加
df = df.append(pd.DataFrame([new_row],index=['e'],columns=df.columns))
Or for single row (Thanks @Zero)
或单行（感谢@Zero）
df = df.append(pd.Series(new_row, index=df.columns, name='e'))
Output:
输出：
number variable values a NaN bank True b 3.0 shop False c 0.5 market True d NaN government True e 1.0 hotel true
回答by Kim Miller
If it's the first row you need:
如果它是您需要的第一行：
df = Dataframe(columns=[number, variable, values])
df.loc['e', [number, variable, values]] = [1.0, 'hotel', 'true']
