Python 在 Pandas 数据框中的每一列中打印唯一值
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print the unique values in every column in a pandas dataframe
提问by yoshiserry
I have a dataframe (df) and want to print the unique values from each column in the dataframe.
我有一个数据框 (df)，想打印数据框中每一列的唯一值。
I need to substitute the variable (i) [column name] into the print statement
我需要将变量 (i) [列名] 替换到打印语句中
column_list = df.columns.values.tolist()
for column_name in column_list:
print(df."[column_name]".unique()
Update
更新
When I use this: I get "Unexpected EOF Parsing"with no extra details.
当我使用它时：我得到“意外的 EOF 解析”，没有额外的细节。
column_list = sorted_data.columns.values.tolist()
for column_name in column_list:
print(sorted_data[column_name].unique()
What is the difference between your syntax YS-L (above) and the below:
你的语法 YS-L（上面）和下面的有什么区别：
for column_name in sorted_data:
print(column_name)
s = sorted_data[column_name].unique()
for i in s:
print(str(i))
回答by YS-L
It can be written more concisely like this:
可以更简洁地写成这样：
for col in df:
print(df[col].unique())
Generally, you can access a column of the DataFrame through indexingusing the []operator (e.g. df['col']), or through attribute(e.g. df.col).
通常，您可以通过使用运算符（例如）或属性（例如）进行索引来访问 DataFrame 的列。[]df['col']df.col
Attribute accessing makes the code a bit more concise when the target column name is known beforehand, but has several caveats -- for example, it does not work when the column name is not a valid Python identifier (e.g. df.123), or clashes with the built-in DataFrame attribute (e.g. df.index). On the other hand, the []notation should always work.
当预先知道目标列名时，属性访问使代码更加简洁，但有几个警告——例如，当列名不是有效的 Python 标识符（例如df.123）时，它不起作用，或者与构建的冲突-in DataFrame 属性（例如df.index）。另一方面，[]符号应该始终有效。
回答by A.Kot
If you're trying to create multiple separate dataframes as mentioned in your comments, create a dictionary of dataframes:
如果您尝试创建评论中提到的多个单独的数据框，请创建一个数据框字典：
df_dict = dict(zip([i for i in df.columns] , [pd.DataFrame(df[i].unique(), columns=[i]) for i in df.columns]))
Then you can access any dataframe easily using the name of the column:
然后您可以使用列的名称轻松访问任何数据框：
df_dict[column name]
回答by bhavin
cu = []
i = []
for cn in card.columns[:7]:
cu.append(card[cn].unique())
i.append(cn)
pd.DataFrame( cu, index=i).T
回答by mgoldwasser
We can make this even more concise:
我们可以让这更简洁：
df.describe(include='all').loc['unique', :]
Pandas describe gives a few key statistics about each column, but we can just grab the 'unique' statistic and leave it at that.
Pandas describe 给出了关于每列的一些关键统计数据，但我们可以只获取“唯一”统计数据并保留它。
Note that this will give a unique count of NaNfor numeric columns - if you want to include those columns as well, you can do something like this:
请注意，这将为NaN数字列提供唯一计数- 如果您还想包括这些列，您可以执行以下操作：
df.astype('object').describe(include='all').loc['unique', :]
回答by Sam Shanmukh
Or in short it can be written as:
或者简而言之，它可以写成：
for val in df['column_name'].unique():
print(val)
回答by Simon Lo
The code below could provide you a list of unique values for each field, I find it very useful when you want to take a deeper look at the data frame:
下面的代码可以为您提供每个字段的唯一值列表，当您想更深入地查看数据框时，我发现它非常有用：
for col in list(df):
print(col)
print(df[col].unique())
You can also sort the unique values if you want them to be sorted:
如果您希望对唯一值进行排序，您还可以对它们进行排序：
import numpy as np
for col in list(df):
print(col)
print(np.sort(df[col].unique()))
