我的pandas数据框如下所示:
Person ID ZipCode Gender
0 12345 882 38182 Female
1 32917 271 88172 Male
2 18273 552 90291 Female
我想复制每一行3次,如:
Person ID ZipCode Gender
0 12345 882 38182 Female
0 12345 882 38182 Female
0 12345 882 38182 Female
1 32917 271 88172 Male
1 32917 271 88172 Male
1 32917 271 88172 Male
2 18273 552 90291 Female
2 18273 552 90291 Female
2 18273 552 90291 Female
当然重置索引所以它是:
0
1
2
我尝试了以下解决方案:
pd.concat([df[:5]]*3, ignore_index=True)
和
df.reindex(np.repeat(df.index.values, df['ID']), method='ffill')
我没有运气,如果你能提供帮助我会很感激。
答案 0 :(得分:11)
试试这个:
newdf = pd.DataFrame(np.repeat(df.values,3,axis=0))
newdf.columns = df.columns
print(newdf)
输出:
Person ID ZipCode Gender
0 12345 882 38182 Female
1 12345 882 38182 Female
2 12345 882 38182 Female
3 32917 271 88172 Male
4 32917 271 88172 Male
5 32917 271 88172 Male
6 18273 552 90291 Female
7 18273 552 90291 Female
8 18273 552 90291 Female
答案 1 :(得分:4)
这些将重复索引并保留列,如操作演示
No images to push
版本1 iloc
df.iloc[np.arange(len(df)).repeat(3)]
版本2 iloc
答案 2 :(得分:2)
你可以这样做。
def do_things(df, n_times):
ndf = df.append(pd.DataFrame({'name' : np.repeat(df.name.values, n_times) }))
ndf = ndf.sort_values(by='name')
ndf = ndf.reset_index(drop=True)
return ndf
if __name__ == '__main__':
df = pd.DataFrame({'name' : ['Peter', 'Quill', 'Jackson']})
n_times = 3
print do_things(df, n_times)
并有解释......
import pandas as pd
import numpy as np
n_times = 3
df = pd.DataFrame({'name' : ['Peter', 'Quill', 'Jackson']})
# name
# 0 Peter
# 1 Quill
# 2 Jackson
# Duplicating data.
df = df.append(pd.DataFrame({'name' : np.repeat(df.name.values, n_times) }))
# name
# 0 Peter
# 1 Quill
# 2 Jackson
# 0 Peter
# 1 Peter
# 2 Peter
# 3 Quill
# 4 Quill
# 5 Quill
# 6 Jackson
# 7 Jackson
# 8 Jackson
# The DataFrame is sorted by 'name' column.
df = df.sort_values(by=['name'])
# name
# 2 Jackson
# 6 Jackson
# 7 Jackson
# 8 Jackson
# 0 Peter
# 0 Peter
# 1 Peter
# 2 Peter
# 1 Quill
# 3 Quill
# 4 Quill
# 5 Quill
# Reseting the index.
# You can play with drop=True and drop=False, as parameter of `reset_index()`
df = df.reset_index()
# index name
# 0 2 Jackson
# 1 6 Jackson
# 2 7 Jackson
# 3 8 Jackson
# 4 0 Peter
# 5 0 Peter
# 6 1 Peter
# 7 2 Peter
# 8 1 Quill
# 9 3 Quill
# 10 4 Quill
# 11 5 Quill
答案 3 :(得分:1)
也许使用concat
pd.concat([df]*3).sort_index()
Out[129]:
Person ID ZipCode Gender
0 12345 882 38182 Female
0 12345 882 38182 Female
0 12345 882 38182 Female
1 32917 271 88172 Male
1 32917 271 88172 Male
1 32917 271 88172 Male
2 18273 552 90291 Female
2 18273 552 90291 Female
2 18273 552 90291 Female