在包含文本文件中字符串信息的字典中，其中键是字符串，值是文件的名称。
Dict1 = {'str1A':'file1', 'str1B':'file1', 'str1C':'file1', 'str1D':'file1', 'str2A':'file2', 'str2B':'file2', 'str2C':'file2', 'str2D':'file2', 'str2D':'file2', 'str3A':'file3',
＆＃39; str3B＆＃39;：＆＃39; file3＆＃39;，＆＃39; str3C＆＃39;：＆＃39; file3＆＃39;，＆＃39; str3D＆＃39;：＆＃ 39; file3＆＃39;，＆＃39; str3D＆＃39;：＆＃39; file3＆＃39; ，＆＃39; str4A＆＃39;：＆＃39; file4＆＃39;，＆＃39; str4B＆＃39;：＆＃39; file4＆＃39;，＆＃39; str4C＆＃39;：＆＃39; file4＆＃39;，＆＃39; str4D＆＃39;：＆＃39; file4＆＃39;，＆＃39; str4E＆＃39;：＆＃39; file4＆＃39;}
另一个字典包含有关文本中字符串最佳匹配的信息。
Dict2 = {'str1A':'jump', 'str1B':'fly', 'str1C':'swim', 'str2A':'jump', 'str2B':'fly', 'str2C':'swim', 'str2D':'run', 'str3A':'jump', 'str3B':'fly', 'str3C':'swim', 'str3D':'run'}
第三个字典包含有关文本中字符串出现百分比的信息。
Dict3 = {'str1A':'90', 'str1B':'60', 'str1C':'30', 'str2A':'70', 'str2B':'30', 'str2C':'60', 'str2D':'40', 'str3A':'10', 'str3B':'90', 'str3C':'70', 'str3D':'90'}
现在我的目标是使用这些不同词典的信息来生成这样的数据框：
jump fly swim run
file1 90 60 30 NA
file2 70 30 60 40
file3 10 90 70 90
对此，我开始编写剧本，但我被卡住了：
col_file = ['str', 'file']
df_origin = pd.DataFrame(Dict1.items(), columns=col_file)
#print df_origin
col_bmatch = ['str', 'text']
df_bmatch = pd.DataFrame(Dict2.items(), columns=col_bmatch)
#print df_bmatch
col_percent = ['str', 'percent']
df_percent = pd.DataFrame(Dict3.items(), columns=col_percent)
#print df_percent
此块已从脚本中删除：
df_origin['text'] = df_origin['str'].map(df_bmatch.set_index('str')['text']) df_origin['percent'] = df_origin['str'].map(df_percent.set_index('str')['percent'])
并替换为：
data = {}
for k, col in Dict1.items():
if k in Dict1 and k not in Dict3:
data.setdefault(k, {})[col] = "NA"
elif k in Dict1 and k in Dict3:
data.setdefault(k, {})[col] = Dict3[k]
df = pd.DataFrame(data)
print(df)
但最终结果并不十分准确：
str1A str1B str1C str1D str2A str2B str2C str2D str3A str3B \
file1 90 60 30 NO NaN NaN NaN NaN NaN NaN
file2 NaN NaN NaN NaN 70 30 60 40 NaN NaN
file3 NaN NaN NaN NaN NaN NaN NaN NaN 10 90
file4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
str3C str3D str4A str4B stre4C str4D str4E
file1 NaN NaN NaN NaN NaN NaN NaN
file2 NaN NaN NaN NaN NaN NaN NaN
file3 70 90 NaN NaN NaN NaN NaN
file4 NaN NaN NO NO NO NO NO
但是预期的表是：
jump fly swim run sit
file1 90 60 30 NA NA
file2 70 30 60 40 NA
file3 10 90 70 90 NA
file4 NA NA NA NA NA
file4中的字符串未检测到的地方。
Blosk已删除
print df_origin # str file text percent # 0 str2B file2 fly 30 # 1 str2C file2 swim 60 # 2 str3C file3 swim 70 # 3 str3B file3 fly 90 # 4 str3D file3 run 90 # 5 str2D file2 run 40 # 6 str3A file3 jump 10 # 7 str1D file1 NaN NaN # 8 str1C file1 swim 30 # 9 str1B file1 fly 60 # 10 str1A file1 jump 90 # 11 str2A file2 jump 70
这里依赖于问题
print pd.get_dummies(df_origin.set_index('file')['text']).max(level=0).max(level=0, axis=1)
但我得到的唯一结果是：
fly jump run swim
file
file2 1 1 1 1
file3 1 1 1 1
file1 1 1 0 1
据我所知，pd.getdummies对字段＆＃39;文件＆＃39;进行分组。来自我的df_origin并使用＆＃39; text＆＃39;检查他们的存在。
如何重定向命令以绘制列＆＃39;％＆＃39;在我的df_origin数据框中？
答案 0 :(得分：2)
试试这个：
import pandas as pd
Dict1 = {'str1A':'file1', 'str1B':'file1', 'str1C':'file1', 'str1D':'file1', 'str2A':'file2', 'str2B':'file2', 'str2C':'file2', 'str2D':'file2', 'str2D':'file2', 'str3A':'file3', 'str3B':'file3','str3C':'file3', 'str3D':'file3', 'str3D':'file3' , 'str4A':'file4', 'str4B':'file4', 'str4C':'file4', 'str4D':'file4', 'str4E':'file4'}
Dict2 = {'str1A':'jump', 'str1B':'fly', 'str1C':'swim', 'str2A':'jump', 'str2B':'fly', 'str2C':'swim', 'str2D':'run', 'str3A':'jump', 'str3B':'fly', 'str3C':'swim', 'str3D':'run'}
Dict3 = {'str1A':'90', 'str1B':'60', 'str1C':'30', 'str2A':'70', 'str2B':'30', 'str2C':'60', 'str2D':'40', 'str3A':'10', 'str3B':'90', 'str3C':'70', 'str3D':'90'}
data = {}
for k, col in Dict2.items():
if k not in Dict1 or k not in Dict3:
continue
data.setdefault(col, {})[Dict1[k]] = Dict3[k]
df = pd.DataFrame(data, index=sorted(set(Dict1.values())), columns=sorted(set(Dict2.values())))
print(df)
输出：
fly jump run swim
file1 60 90 NaN 30
file2 30 70 40 60
file3 90 10 90 70
file4 NaN NaN NaN NaN
答案 1 :(得分：2)
使用pivot。
Dict1 = {'str1A':'file1', 'str1B':'file1', 'str1C':'file1', 'str1D':'file1', 'str2A':'file2', 'str2B':'file2', 'str2C':'file2', 'str2D':'file2', 'str2D':'file2', 'str3A':'file3', 'str3B':'file3','str3C':'file3', 'str3D':'file3', 'str3D':'file3'}
Dict2 = {'str1A':'jump', 'str1B':'fly', 'str1C':'swim', 'str2A':'jump', 'str2B':'fly', 'str2C':'swim', 'str2D':'run', 'str3A':'jump', 'str3B':'fly', 'str3C':'swim', 'str3D':'run'}
Dict3 = {'str1A':'90', 'str1B':'60', 'str1C':'30', 'str2A':'70', 'str2B':'30', 'str2C':'60', 'str2D':'40', 'str3A':'10', 'str3B':'90', 'str3C':'70', 'str3D':'90'}
col_file = ['str', 'file']
df_origin = pd.DataFrame.from_dict(Dict1, orient="index")
df_bmatch = pd.DataFrame.from_dict(Dict2, orient="index")
df_percent = pd.DataFrame.from_dict(Dict3, orient="index")
df_temp = pd.concat([df_origin, df_bmatch, df_percent], axis=1)
df_temp.columns = ["col1", "col2", "col3"]
col1 col2 col3
str1A file1 jump 90
str1B file1 fly 60
str1C file1 swim 30
str1D file1 NaN NaN
str2A file2 jump 70
str2B file2 fly 30
str2C file2 swim 60
str2D file2 run 40
str3A file3 jump 10
str3B file3 fly 90
str3C file3 swim 70
str3D file3 run 90
df_temp.pivot(values="col3", columns="col2", index="col1").drop([np.nan], axis=1)
col2 fly jump run swim
col1
file1 60 90 None 30
file2 30 70 40 60
file3 90 10 90 70