大 pandas DataFrame的单个列中的多个值 [英] Multiple values in single column of a pandas DataFrame

问题描述

我有一些数据要从XML解析到pandas DataFrame. XML数据大致如下所示:

I have some data that I'm parsing from XML to a pandas DataFrame. The XML data roughly looks like this:

<tracks>
  <track name="trackname1" variants="1,2,3,4,5">
    <variant var="1,2,3">
      <leg time="21:23" route_id="5" stop_id="103" serial="1"/>
      <leg time="21:26" route_id="5" stop_id="17" serial="2"/>
      <leg time="21:30" route_id="5" stop_id="38" serial="3"/>
      <leg time="20:57" route_id="8" stop_id="101" serial="1"/>
      <leg time="21:01" route_id="8" stop_id="59" serial="2"/>
      ...
    </variant>
    <variant var="4,5">
      ... more leg elements
    </variant>
  </track>
  <track name="trackname2" variants="1,2,3,4,5,6,7">
    <variant var="1">
      ... more leg elements
    </variant>
    <variant var="2,3,4,5,7">
      ... more leg elements
    </variant>
  </track>
</tracks>

我将其导入到大熊猫中，因为我需要能够将此数据与其他DataFrames连接起来，并且需要能够查询诸如:为变量1的所有支路获取route_id 5"之类的东西.

I'm importing this into pandas because I need to be able to join this data with other DataFrames and I need to be able to query for stuff like: "get all legs of variant 1 for route_id 5".

我试图弄清楚如何在pandas DataFrame中做到这一点.我是否应该制作一个看起来像这样的DataFrame:

I'm trying to figure out how I would do this in a pandas DataFrame. Should I make a DataFrame that would look something like this:

track_name     variants  time     route_id  stop_id  serial
"trackname1"   "1,2,3"   "21:23"  "5"       "103"    "1"
"trackname1"   "1,2,3"   "21:26"  "5"       "17"     "2"
...
"trackname1"   "4,5"     "21:20"  "5"       "103"    "1"
...
"trackname2"   "1"       "20:59"  "3"       "45"     "1"
... you get the point

如果这是可行的方法，我将如何(有效地)提取"route_id 5上变体3的所有行"?请注意，这应该给我所有在变体列列表中具有3的行，而不仅仅是在变体列中仅 具有"3"的行.

If this is the way to go, how would I (efficiently) extract for example "all rows for variant 3 on route_id 5"? Note that this should give me all the rows that have 3 in the variant column list, not just the rows that only have "3" in the variants column.

是否存在一种不同的方式来构造DataFrame，这将使此操作变得更加容易?我应该使用除熊猫以外的其他东西吗?

Is there a different way of constructing the DataFrame that would make this easier? Should I be using something other than pandas?

推荐答案

假设您有足够的内存，如果您的DataFrame每行持有一个变体，则将更容易完成任务:

Assuming you have enough memory, your task will be more easily accomplished if your DataFrame held one variant per row:

track_name     variants  time     route_id  stop_id  serial
"trackname1"   1         "21:23"         5      103       1
"trackname1"   2         "21:23"         5      103       1
"trackname1"   3         "21:23"         5      103       1
"trackname1"   1         "21:26"         5       17       2
"trackname1"   2         "21:26"         5       17       2
"trackname1"   3         "21:26"         5       17       2
...
"trackname1"   4         "21:20"         5      103       1
"trackname1"   5         "21:20"         5      103       1
...
"trackname2"   1         "20:59"         3       45       1

然后，您可以找到"route_id 5上变体3的所有行，

Then you could find "all rows for variant 3 on route_id 5 with

df.loc[(df['variants']==3) & (df['route_id']==5)]

如果您将许多变体打包到一行中，例如

If you pack many variants into one row, such as

"trackname1"   "1,2,3"   "21:23"  "5"       "103"    "1"

然后您可以使用

df.loc[(df['variants'].str.contains("3")) & (df['route_id']=="5")]

假定，变体始终为单个数字.如果还有2位数字的变体，例如"13"或"30"，则需要将更复杂的正则表达式模式传递给str.contains.

assuming that the variants are always single digits. If there are also 2-digit variants like "13" or "30", then you would need to pass a more complicated regex pattern to str.contains.

或者，您可以使用apply在逗号上分割每个变体:

Alternatively, you could use apply to split each variant on commas:

df['variants'].apply(lambda x: "3" in x.split(','))

但这效率很低，因为您现在要调用Python函数 每行一次，并进行字符串拆分和测试成员资格 列表与矢量化整数比较相比.

but this is very inefficent since you would now be calling a Python function once for every row, and doing string splitting and a test for membership in a list compared to a vectorized integer comparision.

因此，为避免可能复杂的正则表达式或对apply的调用相对较慢，我认为最好的选择是构建每行具有一个整数变体的DataFrame.

Thus, to avoid possibly complicated regex or a relatively slow call to apply, I think your best bet is to build the DataFrame with one integer variant per row.

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