你好，我正在使用 python 對(duì)一些數(shù)據(jù)進(jìn)行分類：Articles FilenameA New Marine Ascomycete from Brunei. Invasive Species.csvA new genus and four new species Forestry.csvA new genus and four new species Invasive Species.csv我想知道每個(gè)“文件名”有多少個(gè)獨(dú)特的“文章”。所以我想要的輸出是這樣的：Filename Count_UniqueInvasive Species.csv 1Forestry.csv 0另一件事，我也想得到這個(gè)輸出：Filename1 Filename2 Count_Common articlesForestry.csv Invasive Species.csv 1我連接了數(shù)據(jù)集并最終計(jì)算了每個(gè)“文件名”中存在的元素。有誰愿意幫忙嗎？我已經(jīng)嘗試過unique(), drop_duplicates()等，但似乎我無法得到我想要的輸出。無論如何，這是我的代碼的最后幾行：concatenated = pd.concat(data, ignore_index =True)concatenatedconcatenated.groupby(['Title','Filename']).count().reset_index()res = {col:concatenated[col].value_counts() for col in concatenated.columns}res ['Filename']
1 回答
胡說叔叔
# print
TA貢獻(xiàn)1804條經(jīng)驗(yàn) 獲得超8個(gè)贊
沒有魔法。只是一些常規(guī)操作。
(1) 統(tǒng)計(jì)文件中“獨(dú)特”的文章
編輯：添加（快速而骯臟）代碼以包含計(jì)數(shù)為零的文件名
# prevent repetitive counting
df = df.drop_duplicates()
# articles to be removed (the ones appeared more than once)
dup_articles = df["Articles"].value_counts()
dup_articles = dup_articles[dup_articles > 1].index
# remove duplicate articles and count
mask_dup_articles = df["Articles"].isin(dup_articles)
df_unique = df[~mask_dup_articles]
df_unique["Filename"].value_counts()
# N.B. all filenames not shown here of course has 0 count.
# I will add this part later on.
Out[68]:
Invasive Species.csv 1
Name: Filename, dtype: int64
# unique article count with zeros
df_unique_nonzero_count = df_unique["Filename"].value_counts().to_frame().reset_index()
df_unique_nonzero_count.columns = ["Filename", "count"]
df_all_filenames = pd.DataFrame(
data={"Filename": df["Filename"].unique()}
)
# join: all filenames with counted filenames
df_unique_count = df_all_filenames.merge(df_unique_nonzero_count, on="Filename", how="outer")
# postprocess
df_unique_count.fillna(0, inplace=True)
df_unique_count["count"] = df_unique_count["count"].astype(int)
df_unique_count
Out[119]:
Filename count
0 Invasive Species.csv 1
1 Forestry.csv 0
(2)統(tǒng)計(jì)文件之間的共同文章
# pick out records containing duplicate articles
df_dup = df[mask_dup_articles]
# merge on articles and then discard self- and duplicate pairs
df_merge = df_dup.merge(df_dup, on=["Articles"], suffixes=("1", "2"))
df_merge = df_merge[df_merge["Filename1"] > df_merge["Filename2"]] # alphabetical ordering
# count
df_ans2 = df_merge.groupby(["Filename1", "Filename2"]).count()
df_ans2.reset_index(inplace=True) # optional
df_ans2
Out[70]:
Filename1 Filename2 Articles
0 Invasive Species.csv Forestry.csv 1
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