pandas 忽略数据框中的 NaN
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Ignoring NaN in a dataframe
提问by mihir shanvir
I want to find the unique elements in a column of a dataframe which have missing values. i tried this: df[Column_name].unique()but it returns nan as one of the elements. what can i do to just ignore the missing values.
dataframe look like this.click here
我想在数据框的一列中找到具有缺失值的唯一元素。我试过这个：df[Column_name].unique()但它返回 nan 作为元素之一。我能做些什么来忽略缺失的值。数据框看起来像这样。点击这里
回答by Peter Leimbigler
Try calling .dropna()right before your call to .unique(). A working example:
尝试.dropna()在您致电 之前立即致电.unique()。一个工作示例：
import pandas as pd
import numpy as np
df = pd.DataFrame({'col1': np.random.randint(0, 10, 12)})
df.loc[2] = np.nan
df.loc[5] = np.nan
df['col1'].unique()
### output: array([ 4., 0., nan, 8., 1., 3., 2., 6.])
df['col1'].dropna().unique()
### output: array([ 4., 0., 8., 1., 3., 2., 6.])
