ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

概要

pandas.DataFrameで複数条件を指定したときによく出るエラーの対処方法

df[df['var1'] >= 0 and df['var2'] <= 0.5]

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

解決方法

各条件を括弧でくくる
and/orではなく記号（＆や|）で接続する

df[(df['var1'] >= 0) & (df['var2'] <= 0.5)]

詳細

準備

import numpy as np
import pandas as pd

cols = ['var1', 'var2', 'var3', 'var4']
df = pd.DataFrame(np.random.randn(4, 4), columns=cols)
df

       var1      var2      var3      var4
0  0.597118 -0.853204  1.813645  0.694750
1 -1.118426  0.011119 -2.161933  0.792262
2  0.665828  0.384975  1.676278 -0.487037
3 -0.216118  2.084042 -0.279242 -1.785128

エラーその1

df[df['var1'] >= 0 and df['var2'] <= 0.5]

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

エラーその2

df[(df['var1'] >= 0) and (df['var2'] <= 0.5)]

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

エラーその3

df[df['var1'] >= 0 & df['var2'] <= 0.5]

TypeError: cannot compare a dtyped [float64] array with a scalar of type [bool]

正常

df[(df['var1'] >= 0) & (df['var2'] <= 0.5)]

       var1      var2      var3      var4
0  0.597118 -0.853204  1.813645  0.694750
2  0.665828  0.384975  1.676278 -0.487037

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