blocks|key|4698110|text|索引是一个对象，默认索引从0开始|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4698111|>>>+result.index
Int64Index([0,+1,+2],+dtype=int64)|code-block|syntax|javascript|4698112|您可以使用以下命令按1移位此索引|4698113|>>>+result.index+%2B=+1+
>>>+result.index
Int64Index([1,+2,+3],+dtype=int64)|4698114|entityMap^0|D|1|0|0|A|1|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|Y|8|@]|D|@]|E|$]]]|P|$]]

Index is an object, and default index starts from <code>0</code>:

<pre><code>&gt;&gt;&gt; result.index
Int64Index([0, 1, 2], dtype=int64)
</code></pre>

You can shift this index by <code>1</code> with

<pre><code>&gt;&gt;&gt; result.index += 1 
&gt;&gt;&gt; result.index
Int64Index([1, 2, 3], dtype=int64)
</code></pre>

blocks|key|192377|text|只需在写入CSV之前设置索引即可。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|192378|df.index+=+np.arange(1,+len(df))|code-block|syntax|javascript|192379|然后正常地编写它。|192380|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Just set the index before writing to CSV. 

<pre><code>df.index = np.arange(1, len(df))
</code></pre>

And then write it normally.

blocks|key|4698138|text|来源：In+Python+pandas,+start+row+index+from+1+instead+of+zero+without+creating+additional+column|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4698139|工作示例：|4698140|import+pandas+as+pdas
dframe+=+pdas.read_csv(open(input_file))
dframe.index+=+dframe.index+%2B+1|code-block|syntax|javascript|4698141|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/32249960/in-python-pandas-start-row-index-from-1-instead-of-zero-without-creating-additi/32249984#32249984^0|3|2J|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@$A|T|B|U|1|V]]|C|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|X|8|@]|9|@]|C|$I|J]]|$1|K|3|-4|5|6|7|Y|8|@]|9|@]|C|$]]]|L|$M|$5|N|O|P|C|$Q|R]]]]

source: <a href="https://stackoverflow.com/questions/32249960/in-python-pandas-start-row-index-from-1-instead-of-zero-without-creating-additi/32249984#32249984">In Python pandas, start row index from 1 instead of zero without creating additional column</a>

Working example:

<pre><code>import pandas as pdas
dframe = pdas.read_csv(open(input_file))
dframe.index = dframe.index + 1
</code></pre>

blocks|key|4698146|text|另一种方式是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4698147|df.shift()[1:]|code-block|syntax|javascript|4698148|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Another way in one line:

<pre><code>df.shift()[1:]
</code></pre>

blocks|key|192436|text|这对我很有效|type|unstyled|depth|inlineStyleRanges|entityRanges|data|192437|+df.index+=+np.arange(1,+len(df)%2B1)|code-block|syntax|javascript|192438|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This worked for me

<pre><code> df.index = np.arange(1, len(df)+1)
</code></pre>

blocks|key|192471|text|你可以使用这个：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|192472|import+pandas+as+pd

result+=+pd.DataFrame({'Count':+[83,+19,+20]})
result.index+%2B=+1
print(result)|code-block|syntax|javascript|192473|或者，通过获得numpy库的帮助，如下所示：|offset|length|style|CODE|192474|import+pandas+as+pd
import+numpy+as+np

result+=+pd.DataFrame({'Count':+[83,+19,+20]})
result.index+=+np.arange(1,+len(result)%2B1)
print(result)|192475|np.arange将创建一个numpy数组并在给定间隔内返回值，该间隔为(1,+len(result)%2B1)，最后将该数组赋给result.index。|192476|entityMap^0|0|0|7|5|0|0|0|9|10|I|1R|C|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@$I|V|J|W|K|L]]|9|@]|A|$]]|$1|M|3|N|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|Y|8|@$I|Z|J|10|K|L]|$I|11|J|12|K|L]|$I|13|J|14|K|L]]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|R|$]]

You can use this one:

<pre><code>import pandas as pd

result = pd.DataFrame({'Count': [83, 19, 20]})
result.index += 1
print(result)
</code></pre>

or this one, by getting the help of <code>numpy</code> library like this:

<pre><code>import pandas as pd
import numpy as np

result = pd.DataFrame({'Count': [83, 19, 20]})
result.index = np.arange(1, len(result)+1)
print(result)
</code></pre>

<code>np.arange</code> will create a numpy array and return values within a given interval which is <code>(1, len(result)+1)</code> and finally you will assign that array to <code>result.index</code>.

blocks|key|192492|text|使用这个|type|unstyled|depth|inlineStyleRanges|entityRanges|data|192493|df.index+=+np.arange(1,+len(df)%2B1)|code-block|syntax|javascript|192494|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

use this
<pre><code>df.index = np.arange(1, len(df)+1)
</code></pre>

blocks|key|192506|text|在我看来，最好的方法是使用RangeIndex设置索引|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|192507|import+pandas+as+pd

result+=+pd.DataFrame({'Count':+[83,+19,+20]},+
++++++++++++++++++++++index=pd.RangeIndex(start=1,+stop=4,+name='index')
+++++++++++++++++++++)
>>>+result
+++++++Count
index+++++++
1+++++++++83
2+++++++++19
3+++++++++20|code-block|syntax|javascript|192508|我更喜欢这样，因为您可以在一行中定义索引的范围、可能的step和name。|192509|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.RangeIndex.html?highlight=rangeindex#pandas.RangeIndex^0|D|A|D|A|0|0|0|R|4|W|4|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@$9|X|A|Y|1|Z]]|E|$]]|$1|F|3|G|5|H|7|10|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|11|8|@$9|12|A|13|B|C]|$9|14|A|15|B|C]]|D|@]|E|$]]|$1|M|3|-4|5|6|7|16|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

In my opinion best prectice is to set the index with a <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.RangeIndex.html?highlight=rangeindex#pandas.RangeIndex" rel="nofollow noreferrer"><code>RangeIndex</code></a>
<pre><code>import pandas as pd

result = pd.DataFrame({'Count': [83, 19, 20]}, 
 index=pd.RangeIndex(start=1, stop=4, name='index')
 )
&gt;&gt;&gt; result
 Count
index 
1 83
2 19
3 20
</code></pre>
I prefer this, because you can define the range and a possible <code>step</code> and a <code>name</code> for the index in one line.

blocks|key|192516|text|这将添加一列，用于完成您想要的操作|type|unstyled|depth|inlineStyleRanges|entityRanges|data|192517|df.insert(0,"Column+Name",+np.arange(1,len(df)%2B1))|code-block|syntax|javascript|192518|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This adds a column that accomplishes what you want
<pre class="lang-py prettyprint-override"><code>df.insert(0,&quot;Column Name&quot;, np.arange(1,len(df)+1))
</code></pre>

I need the index to start at 1 rather than 0 when writing a Pandas DataFrame to CSV.

Here's an example:

<pre><code>In [1]: import pandas as pd

In [2]: result = pd.DataFrame({'Count': [83, 19, 20]})

In [3]: result.to_csv('result.csv', index_label='Event_id') 
</code></pre>

Which produces the following output:

<pre><code>In [4]: !cat result.csv
Event_id,Count
0,83
1,19
2,20
</code></pre>

But my desired output is this:

<pre><code>In [5]: !cat result2.csv
Event_id,Count
1,83
2,19
3,20
</code></pre>

I realize that this could be done by adding a sequence of integers shifted by 1 as a column to my data frame, but I'm new to Pandas and I'm wondering if a cleaner way exists.

start index at 1 for Pandas DataFrame

我需要索引从1开始，而不是0，当写入一个熊猫DataFrame到CSV。下面是一个例子：In [1]: import pandas as pdIn [2]: result = pd.DataFrame({'Count': [83, 19, 20]})In [3]: result.to_csv('result.csv', index_label='Event_id')                

问Pandas DataFrame的起始索引为1
EN

回答 9

Stack Overflow用户

Stack Overflow用户

Stack Overflow用户

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问Pandas DataFrame的起始索引为1EN

回答 9

Stack Overflow用户

Stack Overflow用户

Stack Overflow用户

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问Pandas DataFrame的起始索引为1
EN