我在csv中有多个列。如何使用python将行值与引用列匹配？

我有一个包含367列的

csv

文件。第一列有15个唯一值，随后的每一列都有这15个值的子集。在一列中不会多次找到唯一值。每列都已排序。我如何让行对齐？我的最终目标是制作一个存在/不存在热图，但我需要先获得正确格式的数据矩阵，这是我正在努力解决的问题

以下是我拥有的数据类型的一个小示例：

1,2,1,2
2,3,2,5
3,4,3,
4,,5,
5,,,

我需要行与引用匹配，但保持在同一列中，如下所示：

1,,1,
2,2,2,2
3,3,3,
4,4,,
5,,5,5

---

我的想法是使用

pandas

库，但我不知道如何解决这个问题，因为我对使用python非常陌生。我正在使用python2.7。

因此，您的问题肯定可以通过

熊猫
解决：

代码：

# Create the sample data into a data frame
import pandas as pd
from io import StringIO
df = pd.read_csv(StringIO(u"""
    1,2,1,2
    2,3,2,5
    3,4,3,
    4,,5,
    5,,,"""), header=None, skip_blank_lines=1).fillna(0)
for column in df:
    df[column] = pd.to_numeric(df[column], downcast='integer')

# set the first column as an index
df = df.set_index([0])

# create a frame which we will build up 
results = pd.DataFrame(index=df.index)

# add each column to the datafarme indicating if the desired value is present
for col in df.columns:
    results[col] = df.index.isin(df[col])

# output the dataframe in the desired format
for idx, row in results.iterrows():
    result = '%s,%s' % (idx, ','.join(str(idx) if x else ''
                                      for x in row.values))
    print(result)

1,,1,
2,2,2,2
3,3,3,
4,4,,
5,,5,5

结果：

# Create the sample data into a data frame
import pandas as pd
from io import StringIO
df = pd.read_csv(StringIO(u"""
    1,2,1,2
    2,3,2,5
    3,4,3,
    4,,5,
    5,,,"""), header=None, skip_blank_lines=1).fillna(0)
for column in df:
    df[column] = pd.to_numeric(df[column], downcast='integer')

# set the first column as an index
df = df.set_index([0])

# create a frame which we will build up 
results = pd.DataFrame(index=df.index)

# add each column to the datafarme indicating if the desired value is present
for col in df.columns:
    results[col] = df.index.isin(df[col])

# output the dataframe in the desired format
for idx, row in results.iterrows():
    result = '%s,%s' % (idx, ','.join(str(idx) if x else ''
                                      for x in row.values))
    print(result)

1,,1,
2,2,2,2
3,3,3,
4,4,,
5,,5,5

它是如何工作的？：

# Create the sample data into a data frame
import pandas as pd
from io import StringIO
df = pd.read_csv(StringIO(u"""
    1,2,1,2
    2,3,2,5
    3,4,3,
    4,,5,
    5,,,"""), header=None, skip_blank_lines=1).fillna(0)
for column in df:
    df[column] = pd.to_numeric(df[column], downcast='integer')

# set the first column as an index
df = df.set_index([0])

# create a frame which we will build up 
results = pd.DataFrame(index=df.index)

# add each column to the datafarme indicating if the desired value is present
for col in df.columns:
    results[col] = df.index.isin(df[col])

# output the dataframe in the desired format
for idx, row in results.iterrows():
    result = '%s,%s' % (idx, ','.join(str(idx) if x else ''
                                      for x in row.values))
    print(result)

1,,1,
2,2,2,2
3,3,3,
4,4,,
5,,5,5

当第一次接触熊猫时，它可能会有点令人畏惧，即使是对一个非常了解蟒蛇的人来说也是如此，所以我将试着从中走出来。我鼓励你做你需要做的事情来克服学习曲线，因为
pandas
对于这种数据操作来说是非常强大的

将数据放入帧中：

# Create the sample data into a data frame
import pandas as pd
from io import StringIO
df = pd.read_csv(StringIO(u"""
    1,2,1,2
    2,3,2,5
    3,4,3,
    4,,5,
    5,,,"""), header=None, skip_blank_lines=1).fillna(0)
for column in df:
    df[column] = pd.to_numeric(df[column], downcast='integer')

# set the first column as an index
df = df.set_index([0])

# create a frame which we will build up 
results = pd.DataFrame(index=df.index)

# add each column to the datafarme indicating if the desired value is present
for col in df.columns:
    results[col] = df.index.isin(df[col])

# output the dataframe in the desired format
for idx, row in results.iterrows():
    result = '%s,%s' % (idx, ','.join(str(idx) if x else ''
                                      for x in row.values))
    print(result)

1,,1,
2,2,2,2
3,3,3,
4,4,,
5,,5,5

这第一段代码除了将示例数据放入
pandas.DataFrame
中之外，什么都不做。您的数据格式没有指定，所以我假设您可以将其放入一个帧中，或者如果您无法将其放入一个帧中，将在这里询问另一个问题，关于将数据放入帧中

import pandas as pd
from io import StringIO
df = pd.read_csv(StringIO(u"""
    1,2,1,2
    2,3,2,5
    3,4,3,
    4,,5,
    5,,,"""), header=None, skip_blank_lines=1).fillna(0)
for column in df:
    df[column] = pd.to_numeric(df[column], downcast='integer')    

# set the first column as an index
df = df.set_index([0])

构建结果框架：

# Create the sample data into a data frame
import pandas as pd
from io import StringIO
df = pd.read_csv(StringIO(u"""
    1,2,1,2
    2,3,2,5
    3,4,3,
    4,,5,
    5,,,"""), header=None, skip_blank_lines=1).fillna(0)
for column in df:
    df[column] = pd.to_numeric(df[column], downcast='integer')

# set the first column as an index
df = df.set_index([0])

# create a frame which we will build up 
results = pd.DataFrame(index=df.index)

# add each column to the datafarme indicating if the desired value is present
for col in df.columns:
    results[col] = df.index.isin(df[col])

# output the dataframe in the desired format
for idx, row in results.iterrows():
    result = '%s,%s' % (idx, ','.join(str(idx) if x else ''
                                      for x in row.values))
    print(result)

1,,1,
2,2,2,2
3,3,3,
4,4,,
5,,5,5

从一个仅仅是索引的结果帧开始

# create a frame which we will build up 
results = pd.DataFrame(index=df.index)

# add each column to the dataframe indicating if the desired value is present
for col in df.columns:
    results[col] = df.index.isin(df[col])

对于源数据中的每一列，请查看该值是否在索引中

# create a frame which we will build up 
results = pd.DataFrame(index=df.index)

# add each column to the dataframe indicating if the desired value is present
for col in df.columns:
    results[col] = df.index.isin(df[col])

就这样，用三行代码，我们计算了结果

输出结果：

# Create the sample data into a data frame
import pandas as pd
from io import StringIO
df = pd.read_csv(StringIO(u"""
    1,2,1,2
    2,3,2,5
    3,4,3,
    4,,5,
    5,,,"""), header=None, skip_blank_lines=1).fillna(0)
for column in df:
    df[column] = pd.to_numeric(df[column], downcast='integer')

# set the first column as an index
df = df.set_index([0])

# create a frame which we will build up 
results = pd.DataFrame(index=df.index)

# add each column to the datafarme indicating if the desired value is present
for col in df.columns:
    results[col] = df.index.isin(df[col])

# output the dataframe in the desired format
for idx, row in results.iterrows():
    result = '%s,%s' % (idx, ','.join(str(idx) if x else ''
                                      for x in row.values))
    print(result)

1,,1,
2,2,2,2
3,3,3,
4,4,,
5,,5,5

现在遍历包含布尔值的每一行，并以所需格式输出值（如int）

这将首先输出索引值，然后对于每个
True
值，再次输出索引，对于
False
值，输出空字符串

附言：

# Create the sample data into a data frame
import pandas as pd
from io import StringIO
df = pd.read_csv(StringIO(u"""
    1,2,1,2
    2,3,2,5
    3,4,3,
    4,,5,
    5,,,"""), header=None, skip_blank_lines=1).fillna(0)
for column in df:
    df[column] = pd.to_numeric(df[column], downcast='integer')

# set the first column as an index
df = df.set_index([0])

# create a frame which we will build up 
results = pd.DataFrame(index=df.index)

# add each column to the datafarme indicating if the desired value is present
for col in df.columns:
    results[col] = df.index.isin(df[col])

# output the dataframe in the desired format
for idx, row in results.iterrows():
    result = '%s,%s' % (idx, ','.join(str(idx) if x else ''
                                      for x in row.values))
    print(result)

1,,1,
2,2,2,2
3,3,3,
4,4,,
5,,5,5

这里有很多人比我更擅长熊猫，但是因为你没有在你的问题上加上熊猫关键字，他们可能没有注意到这个问题。但这让我可以在他们注意到之前接受我的回答。对于格式良好的问题，
pandas
关键字覆盖得很好，因此我非常确信，如果这个答案不是最佳答案，其他人会过来改进它。因此，在将来，一定要用
pandas
标记您的问题，以获得最佳答案

另外，您提到您是新的python，所以我将插入一个插件，以确保您使用的是一个好的IDE。我使用它，而且它和其他优秀的IDE可以使在python中工作更加强大，所以我强烈推荐它们