Python 在熊猫数据框中的顶部添加一行
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add a row at top in pandas dataframe
提问by Shubham R
Below is my dataframe
下面是我的数据框
import pandas as pd
df = pd.DataFrame({'name': ['jon','sam','jane','bob'],
'age': [30,25,18,26],
'sex':['male','male','female','male']})
age name sex
0 30 jon male
1 25 sam male
2 18 jane female
3 26 bob male
I want to insert a new row at the first position
我想在第一个位置插入一个新行
name: dean, age: 45, sex: male
姓名：院长，年龄：45，性别：男
age name sex
0 45 dean male
1 30 jon male
2 25 sam male
3 18 jane female
4 26 bob male
What is the best way to do this in pandas?
在熊猫中做到这一点的最佳方法是什么？
采纳答案by MaxU
If it's going to be a frequent operation, then it makes sense (in terms of performance) to gather the data into a list first and then use pd.concat([], ignore_index=True)(similar to @Serenity's solution):
如果这将是一个频繁的操作，那么首先将数据收集到列表中然后使用pd.concat([], ignore_index=True)（类似于@Serenity 的解决方案）是有意义的（就性能而言）：
Demo:
演示：
data = []
# always inserting new rows at the first position - last row will be always on top
data.insert(0, {'name': 'dean', 'age': 45, 'sex': 'male'})
data.insert(0, {'name': 'joe', 'age': 33, 'sex': 'male'})
#...
pd.concat([pd.DataFrame(data), df], ignore_index=True)
In [56]: pd.concat([pd.DataFrame(data), df], ignore_index=True)
Out[56]:
age name sex
0 33 joe male
1 45 dean male
2 30 jon male
3 25 sam male
4 18 jane female
5 26 bob male
PS I wouldn't call .append(), pd.concat(), .sort_index()too frequently (for each single row) as it's pretty expensive. So the idea is to do it in chunks...
PS 我不会太频繁地（对于每一行）调用.append(), pd.concat()，.sort_index()因为它非常昂贵。所以这个想法是分块做...
回答by edyvedy13
Probably this is not the most efficient way but:
可能这不是最有效的方法，但是：
df.loc[-1] = ['45', 'Dean', 'male'] # adding a row
df.index = df.index + 1 # shifting index
df.sort_index(inplace=True)
Output:
输出：
age name sex
0 45 Dean male
1 30 jon male
2 25 sam male
3 18 jane female
4 26 bob male
回答by Edward Atkins
@edyvedy13's solutionworked great for me. However it needs to be updated for the deprecationof pandas' sortmethod - now replaced with sort_index.
@edyvedy13 的解决方案对我很有用。但是，它需要更新以弃用熊猫的sort方法 - 现在替换为sort_index.
df.loc[-1] = ['45', 'Dean', 'male'] # adding a row
df.index = df.index + 1 # shifting index
df = df.sort_index() # sorting by index
回答by Serenity
Use pandas.concatand reindex new dataframe:
使用pandas.concat并重新索引新数据框：
import pandas as pd
df = pd.DataFrame({'name': ['jon','sam','jane','bob'],
'age': [30,25,18,26],
'sex':['male','male','female','male']})
# new line
line = pd.DataFrame({'name': 'dean', 'age': 45, 'sex': 'male'}, index=[0])
# concatenate two dataframe
df2 = pd.concat([line,df.ix[:]]).reset_index(drop=True)
print (df2)
Output:
输出：
age name sex
0 45 dean male
1 30 jon male
2 25 sam male
3 18 jane female
4 26 bob male
回答by dylan_fan
import pandas as pd
df = pd.DataFrame({'name': ['jon','sam','jane','bob'],
'age': [30,25,18,26],
'sex': ['male','male','female','male']})
df1 = pd.DataFrame({'name': ['dean'], 'age': [45], 'sex':['male']})
df1 = df1.append(df)
df1 = df1.reset_index(drop=True)
That works
那个有效
回答by husimu
This will work for me.
这对我有用。
>>> import pandas as pd
>>> df = pd.DataFrame({'name': ['jon','sam','jane','bob'],
... 'age': [30,25,18,26],
... 'sex':['male','male','female','male']}) >>> df
age name sex
0 30 jon male
1 25 sam male
2 18 jane female
3 26 bob male
>>> df.loc['a']=[45,'dean','male']
>>> df
age name sex
0 30 jon male
1 25 sam male
2 18 jane female
3 26 bob male
a 45 dean male
>>> newIndex=['a']+[ind for ind in df.index if ind!='a']
>>> df=df.reindex(index=newIndex)
>>> df
age name sex
a 45 dean male
0 30 jon male
1 25 sam male
2 18 jane female
3 26 bob male
