blocks|key|4192262|text|使用np.repeat|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4192263|版本1：|4192264|尝试使用np.repeat|4192265|newdf+=+pd.DataFrame(np.repeat(df.values,+3,+axis=0))
newdf.columns+=+df.columns
print(newdf)|code-block|syntax|javascript|4192266|上面的代码将输出：|4192267|++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|4192268|np.repeat重复df，3时间的值。|4192269|然后，我们使用赋值new_df.columns+=+df.columns来添加列。|4192270|版本2：|4192271|您还可以在第一行中指定列名，如下所示：|4192272|newdf+=+pd.DataFrame(np.repeat(df.values,+3,+axis=0),+columns=df.columns)
print(newdf)|4192273|上面的代码还将输出：|4192274|++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|4192275|entityMap|0|LINK|mutability|MUTABLE|url|https://numpy.org/doc/stable/reference/generated/numpy.repeat.html|1|2^0|2|9|2|9|0|0|0|4|9|4|9|1|0|0|0|0|0|9|B|2|E|1|0|9|2|0|9|R|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1G|8|@$9|1H|A|1I|B|C]]|D|@$9|1J|A|1K|1|1L]]|E|$]]|$1|F|3|G|5|6|7|1M|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|1N|8|@$9|1O|A|1P|B|C]]|D|@$9|1Q|A|1R|1|1S]]|E|$]]|$1|J|3|K|5|L|7|1T|8|@]|D|@]|E|$M|N]]|$1|O|3|P|5|6|7|1U|8|@]|D|@]|E|$]]|$1|Q|3|R|5|L|7|1V|8|@]|D|@]|E|$M|N]]|$1|S|3|T|5|6|7|1W|8|@$9|1X|A|1Y|B|C]|$9|1Z|A|20|B|C]|$9|21|A|22|B|C]]|D|@$9|23|A|24|1|25]]|E|$]]|$1|U|3|V|5|6|7|26|8|@$9|27|A|28|B|C]]|D|@]|E|$]]|$1|W|3|X|5|6|7|29|8|@]|D|@]|E|$]]|$1|Y|3|Z|5|6|7|2A|8|@]|D|@]|E|$]]|$1|10|3|11|5|L|7|2B|8|@]|D|@]|E|$M|N]]|$1|12|3|13|5|6|7|2C|8|@]|D|@]|E|$]]|$1|14|3|15|5|L|7|2D|8|@]|D|@]|E|$M|N]]|$1|16|3|-4|5|6|7|2E|8|@]|D|@]|E|$]]]|17|$18|$5|19|1A|1B|E|$1C|1D]]|1E|$5|19|1A|1B|E|$1C|1D]]|1F|$5|19|1A|1B|E|$1C|1D]]]]

<h2>Use <a href="https://numpy.org/doc/stable/reference/generated/numpy.repeat.html" rel="nofollow noreferrer"><code>np.repeat</code></a>:</h2>
<h1>Version 1:</h1>
Try using <a href="https://numpy.org/doc/stable/reference/generated/numpy.repeat.html" rel="nofollow noreferrer"><code>np.repeat</code></a>:
<pre><code>newdf = pd.DataFrame(np.repeat(df.values, 3, axis=0))
newdf.columns = df.columns
print(newdf)
</code></pre>
The above code will output:
<pre><code> 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
</code></pre>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.repeat.html" rel="nofollow noreferrer"><code>np.repeat</code></a> repeats the values of <code>df</code>, <code>3</code> times.
Then we add the columns with assigning <code>new_df.columns = df.columns</code>.
<h1>Version 2:</h1>
You could also assign the column names in the first line, like below:
<pre><code>newdf = pd.DataFrame(np.repeat(df.values, 3, axis=0), columns=df.columns)
print(newdf)
</code></pre>
The above code will also output:
<pre><code> 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
</code></pre>

blocks|key|4191902|text|你可以这样做。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4191903|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)|code-block|syntax|javascript|4191904|有了解释..。|4191905|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|4191906|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

You can do it like this.

<pre><code>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)
</code></pre>

And with explanation...

<pre><code>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
</code></pre>

blocks|key|4192334|text|使用concat|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4192335|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|code-block|syntax|javascript|4192336|entityMap^0|2|6|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

Using <code>concat</code>:
<pre><code>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
</code></pre>

blocks|key|4192388|text|您可以尝试以下代码：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4192389|df+=+df.iloc[df.index.repeat(3),:].reset_index()|code-block|syntax|javascript|4192390|df.index.repeat(3)将创建一个列表，其中每个索引值将重复3次，df.iloc[df.index.repeat(3),:]将帮助生成包含该列表返回的行的数据帧。|offset|length|style|CODE|4192391|entityMap^0|0|0|0|I|13|T|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@$I|R|J|S|K|L]|$I|T|J|U|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|V|8|@]|9|@]|A|$]]]|N|$]]

You can try the following code:
<pre class="lang-py prettyprint-override"><code>df = df.iloc[df.index.repeat(3),:].reset_index()
</code></pre>
<code>df.index.repeat(3)</code> will create a list where each index value will be repeated 3 times and <code>df.iloc[df.index.repeat(3),:]</code> will help generate a dataframe with the rows as exactly returned by this list.

My pandas dataframe looks like this:
<pre><code> Person ID ZipCode Gender
0 12345 882 38182 Female
1 32917 271 88172 Male
2 18273 552 90291 Female
</code></pre>
I want to replicate every row 3 times like:
<pre><code> 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
</code></pre>
And of course, reset the index so it is:
<pre><code>0
1
2
...
</code></pre>
I tried solutions such as:
<pre><code>pd.concat([df[:5]]*3, ignore_index=True)
</code></pre>
And:
<pre><code>df.reindex(np.repeat(df.index.values, df['ID']), method='ffill')
</code></pre>
But none of them worked.

How can I replicate rows in Pandas?

我的pandas数据帧如下所示：   Person  ID   ZipCode   Gender0  12345   882  38182     Female1  32917   271  88172     Male2  18273   552  90291     Female我想将每行复制3次，如下所示：   ...

问如何在Pandas中复制行？
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