Python 如果列在某个范围内，则筛选行

如何根据列中的数字是否在定义的范围内，将行筛选到特定的csv文件中

input.csv
1.23
2.43
5.28
6.42
6.42
6.42
8.98

我正在尝试将

0.01

和

8.99

之间的任何内容输出到

low.csv

，但运行它时没有发生任何事情：

import openpyxl
import csv

low = [x * 0.01 for x in range(0, 900)]

reader = csv.reader(open(r"input.csv"), delimiter=',') 
filtered = filter(lambda p:low == p[0], reader) 
csv.writer(open(r"low.csv",'w'),delimiter=',').writerows(filtered)
print (list(filtered))

使用熊猫

import pandas as pd

df = pd.read_csv("input.csv")
df = df[(df > 0.01) & (df < 8.99)]
df.to_csv("input.csv", index=False)

将熊猫作为pd导入
df=pd.read\u csv（“input.csv”）
df=df[（df>0.01）和（df<8.99）]
df.to_csv（“input.csv”，index=False）

使用熊猫

import pandas as pd

df = pd.read_csv("input.csv")
df = df[(df > 0.01) & (df < 8.99)]
df.to_csv("input.csv", index=False)

将熊猫作为pd导入
df=pd.read\u csv（“input.csv”）
df=df[（df>0.01）和（df<8.99）]
df.to_csv（“input.csv”，index=False）

据我所见，您的

低

变量有。请参阅此输出：

>>> low = [x * 0.01 for x in range(0, 900)]
>>> low
[0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35000000000000003, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41000000000000003, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47000000000000003, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.5700000000000001, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.6900000000000001, 0.7000000000000001, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.8200000000000001, 0.8300000000000001, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.9400000000000001, 0.9500000000000001, 0.96, 0.97, 0.98, 0.99, 1.0, 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.1, 1.11, 1.12, 1.1300000000000001, 1.1400000000000001, 1.1500000000000001, 1.16, 1.17, 1.18, 1.19, 1.2, 1.21, 1.22, 1.23, 1.24, 1.25, 1.26, 1.27, 1.28, 1.29, 1.3, 1.31, 1.32, 1.33, 1.34, 1.35, 1.36, 1.37, 1.3800000000000001, 1.3900000000000001, 1.4000000000000001, 1.41, 1.42, 1.43, 1.44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.5, 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59, 1.6, 1.61, 1.62, 1.6300000000000001, 1.6400000000000001, 1.6500000000000001, 1.6600000000000001, 1.67, 1.68, 1.69, 1.7, 1.71, 1.72, 1.73, 1.74, 1.75, 1.76, 1.77, 1.78, 1.79, 1.8, 1.81, 1.82, 1.83, 1.84, 1.85, 1.86, 1.87, 1.8800000000000001, 1.8900000000000001, 1.9000000000000001, 1.9100000000000001, 1.92, 1.93, 1.94, 1.95, 1.96, 1.97, 1.98, 1.99, 2.0, 2.0100000000000002, 2.02, 2.0300000000000002, 2.04, 2.05, 2.06, 2.07, 2.08, 2.09, 2.1, 2.11, 2.12, 2.13, 2.14, 2.15, 2.16, 2.17, 2.18, 2.19, 2.2, 2.21, 2.22, 2.23, 2.24, 2.25, 2.2600000000000002, 2.27, 2.2800000000000002, 2.29, 2.3000000000000003, 2.31, 2.32, 2.33, 2.34, 2.35, 2.36, 2.37, 2.38, 2.39, 2.4, 2.41, 2.42, 2.43, 2.44, 2.45, 2.46, 2.47, 2.48, 2.49, 2.5, 2.5100000000000002, 2.52, 2.5300000000000002, 2.54, 2.5500000000000003, 2.56, 2.57, 2.58, 2.59, 2.6, 2.61, 2.62, 2.63, 2.64, 2.65, 2.66, 2.67, 2.68, 2.69, 2.7, 2.71, 2.72, 2.73, 2.74, 2.75, 2.7600000000000002, 2.77, 2.7800000000000002, 2.79, 2.8000000000000003, 2.81, 2.82, 2.83, 2.84, 2.85, 2.86, 2.87, 2.88, 2.89, 2.9, 2.91, 2.92, 2.93, 2.94, 2.95, 2.96, 2.97, 2.98, 2.99, 3.0, 3.0100000000000002, 3.02, 3.0300000000000002, 3.04, 3.0500000000000003, 3.06, 3.0700000000000003, 3.08, 3.09, 3.1, 3.11, 3.12, 3.13, 3.14, 3.15, 3.16, 3.17, 3.18, 3.19, 3.2, 3.21, 3.22, 3.23, 3.24, 3.25, 3.2600000000000002, 3.27, 3.2800000000000002, 3.29, 3.3000000000000003, 3.31, 3.3200000000000003, 3.33, 3.34, 3.35, 3.36, 3.37, 3.38, 3.39, 3.4, 3.41, 3.42, 3.43, 3.44, 3.45, 3.46, 3.47, 3.48, 3.49, 3.5, 3.5100000000000002, 3.52, 3.5300000000000002, 3.54, 3.5500000000000003, 3.56, 3.5700000000000003, 3.58, 3.59, 3.6, 3.61, 3.62, 3.63, 3.64, 3.65, 3.66, 3.67, 3.68, 3.69, 3.7, 3.71, 3.72, 3.73, 3.74, 3.75, 3.7600000000000002, 3.77, 3.7800000000000002, 3.79, 3.8000000000000003, 3.81, 3.8200000000000003, 3.83, 3.84, 3.85, 3.86, 3.87, 3.88, 3.89, 3.9, 3.91, 3.92, 3.93, 3.94, 3.95, 3.96, 3.97, 3.98, 3.99, 4.0, 4.01, 4.0200000000000005, 4.03, 4.04, 4.05, 4.0600000000000005, 4.07, 4.08, 4.09, 4.1, 4.11, 4.12, 4.13, 4.14, 4.15, 4.16, 4.17, 4.18, 4.19, 4.2, 4.21, 4.22, 4.23, 4.24, 4.25, 4.26, 4.2700000000000005, 4.28, 4.29, 4.3, 4.3100000000000005, 4.32, 4.33, 4.34, 4.3500000000000005, 4.36, 4.37, 4.38, 4.39, 4.4, 4.41, 4.42, 4.43, 4.44, 4.45, 4.46, 4.47, 4.48, 4.49, 4.5, 4.51, 4.5200000000000005, 4.53, 4.54, 4.55, 4.5600000000000005, 4.57, 4.58, 4.59, 4.6000000000000005, 4.61, 4.62, 4.63, 4.64, 4.65, 4.66, 4.67, 4.68, 4.69, 4.7, 4.71, 4.72, 4.73, 4.74, 4.75, 4.76, 4.7700000000000005, 4.78, 4.79, 4.8, 4.8100000000000005, 4.82, 4.83, 4.84, 4.8500000000000005, 4.86, 4.87, 4.88, 4.89, 4.9, 4.91, 4.92, 4.93, 4.94, 4.95, 4.96, 4.97, 4.98, 4.99, 5.0, 5.01, 5.0200000000000005, 5.03, 5.04, 5.05, 5.0600000000000005, 5.07, 5.08, 5.09, 5.1000000000000005, 5.11, 5.12, 5.13, 5.14, 5.15, 5.16, 5.17, 5.18, 5.19, 5.2, 5.21, 5.22, 5.23, 5.24, 5.25, 5.26, 5.2700000000000005, 5.28, 5.29, 5.3, 5.3100000000000005, 5.32, 5.33, 5.34, 5.3500000000000005, 5.36, 5.37, 5.38, 5.39, 5.4, 5.41, 5.42, 5.43, 5.44, 5.45, 5.46, 5.47, 5.48, 5.49, 5.5, 5.51, 5.5200000000000005, 5.53, 5.54, 5.55, 5.5600000000000005, 5.57, 5.58, 5.59, 5.6000000000000005, 5.61, 5.62, 5.63, 5.64, 5.65, 5.66, 5.67, 5.68, 5.69, 5.7, 5.71, 5.72, 5.73, 5.74, 5.75, 5.76, 5.7700000000000005, 5.78, 5.79, 5.8, 5.8100000000000005, 5.82, 5.83, 5.84, 5.8500000000000005, 5.86, 5.87, 5.88, 5.89, 5.9, 5.91, 5.92, 5.93, 5.94, 5.95, 5.96, 5.97, 5.98, 5.99, 6.0, 6.01, 6.0200000000000005, 6.03, 6.04, 6.05, 6.0600000000000005, 6.07, 6.08, 6.09, 6.1000000000000005, 6.11, 6.12, 6.13, 6.140000000000001, 6.15, 6.16, 6.17, 6.18, 6.19, 6.2, 6.21, 6.22, 6.23, 6.24, 6.25, 6.26, 6.2700000000000005, 6.28, 6.29, 6.3, 6.3100000000000005, 6.32, 6.33, 6.34, 6.3500000000000005, 6.36, 6.37, 6.38, 6.390000000000001, 6.4, 6.41, 6.42, 6.43, 6.44, 6.45, 6.46, 6.47, 6.48, 6.49, 6.5, 6.51, 6.5200000000000005, 6.53, 6.54, 6.55, 6.5600000000000005, 6.57, 6.58, 6.59, 6.6000000000000005, 6.61, 6.62, 6.63, 6.640000000000001, 6.65, 6.66, 6.67, 6.68, 6.69, 6.7, 6.71, 6.72, 6.73, 6.74, 6.75, 6.76, 6.7700000000000005, 6.78, 6.79, 6.8, 6.8100000000000005, 6.82, 6.83, 6.84, 6.8500000000000005, 6.86, 6.87, 6.88, 6.890000000000001, 6.9, 6.91, 6.92, 6.93, 6.94, 6.95, 6.96, 6.97, 6.98, 6.99, 7.0, 7.01, 7.0200000000000005, 7.03, 7.04, 7.05, 7.0600000000000005, 7.07, 7.08, 7.09, 7.1000000000000005, 7.11, 7.12, 7.13, 7.140000000000001, 7.15, 7.16, 7.17, 7.18, 7.19, 7.2, 7.21, 7.22, 7.23, 7.24, 7.25, 7.26, 7.2700000000000005, 7.28, 7.29, 7.3, 7.3100000000000005, 7.32, 7.33, 7.34, 7.3500000000000005, 7.36, 7.37, 7.38, 7.390000000000001, 7.4, 7.41, 7.42, 7.43, 7.44, 7.45, 7.46, 7.47, 7.48, 7.49, 7.5, 7.51, 7.5200000000000005, 7.53, 7.54, 7.55, 7.5600000000000005, 7.57, 7.58, 7.59, 7.6000000000000005, 7.61, 7.62, 7.63, 7.640000000000001, 7.65, 7.66, 7.67, 7.68, 7.69, 7.7, 7.71, 7.72, 7.73, 7.74, 7.75, 7.76, 7.7700000000000005, 7.78, 7.79, 7.8, 7.8100000000000005, 7.82, 7.83, 7.84, 7.8500000000000005, 7.86, 7.87, 7.88, 7.890000000000001, 7.9, 7.91, 7.92, 7.930000000000001, 7.94, 7.95, 7.96, 7.97, 7.98, 7.99, 8.0, 8.01, 8.02, 8.03, 8.040000000000001, 8.05, 8.06, 8.07, 8.08, 8.09, 8.1, 8.11, 8.120000000000001, 8.13, 8.14, 8.15, 8.16, 8.17, 8.18, 8.19, 8.2, 8.21, 8.22, 8.23, 8.24, 8.25, 8.26, 8.27, 8.28, 8.290000000000001, 8.3, 8.31, 8.32, 8.33, 8.34, 8.35, 8.36, 8.370000000000001, 8.38, 8.39, 8.4, 8.41, 8.42, 8.43, 8.44, 8.45, 8.46, 8.47, 8.48, 8.49, 8.5, 8.51, 8.52, 8.53, 8.540000000000001, 8.55, 8.56, 8.57, 8.58, 8.59, 8.6, 8.61, 8.620000000000001, 8.63, 8.64, 8.65, 8.66, 8.67, 8.68, 8.69, 8.700000000000001, 8.71, 8.72, 8.73, 8.74, 8.75, 8.76, 8.77, 8.78, 8.790000000000001, 8.8, 8.81, 8.82, 8.83, 8.84, 8.85, 8.86, 8.870000000000001, 8.88, 8.89, 8.9, 8.91, 8.92, 8.93, 8.94, 8.950000000000001, 8.96, 8.97, 8.98, 8.99]

正如您所看到的，有些数字并不是您所期望的：

8.540000000000001
8.620000000000001
8.700000000000001
8.790000000000001

等等。您应该做的只是更改lambda函数：

>>> # Looking at your range, you seem to want to include 0.01 and 8.99
>>> filtered = filter(lambda x: x>=0.01 and x<=8.99, reader)

您还应注意，如果要查看列表中是否有数字，则应在中使用

，而不是
=
：

>>> # Assuming `low` is a working list
>>> filtered = filter(lambda p: p in low, reader)

编辑

由于您使用的是
csv.reader
，看起来您还需要一些步骤。“更脏”的方法是将
读卡器
扁平化为一个列表：

reader.next() #Remove the header 
l = [float(x[0]) for x in reader]

使用它：

filtered = filter(lambda x: x>=0.01 and x<=8.99, l)

从我看到的情况来看，您的
low
变量具有。请参阅此输出：

>>> low = [x * 0.01 for x in range(0, 900)]
>>> low
[0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35000000000000003, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41000000000000003, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47000000000000003, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.5700000000000001, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.6900000000000001, 0.7000000000000001, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.8200000000000001, 0.8300000000000001, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.9400000000000001, 0.9500000000000001, 0.96, 0.97, 0.98, 0.99, 1.0, 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.1, 1.11, 1.12, 1.1300000000000001, 1.1400000000000001, 1.1500000000000001, 1.16, 1.17, 1.18, 1.19, 1.2, 1.21, 1.22, 1.23, 1.24, 1.25, 1.26, 1.27, 1.28, 1.29, 1.3, 1.31, 1.32, 1.33, 1.34, 1.35, 1.36, 1.37, 1.3800000000000001, 1.3900000000000001, 1.4000000000000001, 1.41, 1.42, 1.43, 1.44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.5, 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59, 1.6, 1.61, 1.62, 1.6300000000000001, 1.6400000000000001, 1.6500000000000001, 1.6600000000000001, 1.67, 1.68, 1.69, 1.7, 1.71, 1.72, 1.73, 1.74, 1.75, 1.76, 1.77, 1.78, 1.79, 1.8, 1.81, 1.82, 1.83, 1.84, 1.85, 1.86, 1.87, 1.8800000000000001, 1.8900000000000001, 1.9000000000000001, 1.9100000000000001, 1.92, 1.93, 1.94, 1.95, 1.96, 1.97, 1.98, 1.99, 2.0, 2.0100000000000002, 2.02, 2.0300000000000002, 2.04, 2.05, 2.06, 2.07, 2.08, 2.09, 2.1, 2.11, 2.12, 2.13, 2.14, 2.15, 2.16, 2.17, 2.18, 2.19, 2.2, 2.21, 2.22, 2.23, 2.24, 2.25, 2.2600000000000002, 2.27, 2.2800000000000002, 2.29, 2.3000000000000003, 2.31, 2.32, 2.33, 2.34, 2.35, 2.36, 2.37, 2.38, 2.39, 2.4, 2.41, 2.42, 2.43, 2.44, 2.45, 2.46, 2.47, 2.48, 2.49, 2.5, 2.5100000000000002, 2.52, 2.5300000000000002, 2.54, 2.5500000000000003, 2.56, 2.57, 2.58, 2.59, 2.6, 2.61, 2.62, 2.63, 2.64, 2.65, 2.66, 2.67, 2.68, 2.69, 2.7, 2.71, 2.72, 2.73, 2.74, 2.75, 2.7600000000000002, 2.77, 2.7800000000000002, 2.79, 2.8000000000000003, 2.81, 2.82, 2.83, 2.84, 2.85, 2.86, 2.87, 2.88, 2.89, 2.9, 2.91, 2.92, 2.93, 2.94, 2.95, 2.96, 2.97, 2.98, 2.99, 3.0, 3.0100000000000002, 3.02, 3.0300000000000002, 3.04, 3.0500000000000003, 3.06, 3.0700000000000003, 3.08, 3.09, 3.1, 3.11, 3.12, 3.13, 3.14, 3.15, 3.16, 3.17, 3.18, 3.19, 3.2, 3.21, 3.22, 3.23, 3.24, 3.25, 3.2600000000000002, 3.27, 3.2800000000000002, 3.29, 3.3000000000000003, 3.31, 3.3200000000000003, 3.33, 3.34, 3.35, 3.36, 3.37, 3.38, 3.39, 3.4, 3.41, 3.42, 3.43, 3.44, 3.45, 3.46, 3.47, 3.48, 3.49, 3.5, 3.5100000000000002, 3.52, 3.5300000000000002, 3.54, 3.5500000000000003, 3.56, 3.5700000000000003, 3.58, 3.59, 3.6, 3.61, 3.62, 3.63, 3.64, 3.65, 3.66, 3.67, 3.68, 3.69, 3.7, 3.71, 3.72, 3.73, 3.74, 3.75, 3.7600000000000002, 3.77, 3.7800000000000002, 3.79, 3.8000000000000003, 3.81, 3.8200000000000003, 3.83, 3.84, 3.85, 3.86, 3.87, 3.88, 3.89, 3.9, 3.91, 3.92, 3.93, 3.94, 3.95, 3.96, 3.97, 3.98, 3.99, 4.0, 4.01, 4.0200000000000005, 4.03, 4.04, 4.05, 4.0600000000000005, 4.07, 4.08, 4.09, 4.1, 4.11, 4.12, 4.13, 4.14, 4.15, 4.16, 4.17, 4.18, 4.19, 4.2, 4.21, 4.22, 4.23, 4.24, 4.25, 4.26, 4.2700000000000005, 4.28, 4.29, 4.3, 4.3100000000000005, 4.32, 4.33, 4.34, 4.3500000000000005, 4.36, 4.37, 4.38, 4.39, 4.4, 4.41, 4.42, 4.43, 4.44, 4.45, 4.46, 4.47, 4.48, 4.49, 4.5, 4.51, 4.5200000000000005, 4.53, 4.54, 4.55, 4.5600000000000005, 4.57, 4.58, 4.59, 4.6000000000000005, 4.61, 4.62, 4.63, 4.64, 4.65, 4.66, 4.67, 4.68, 4.69, 4.7, 4.71, 4.72, 4.73, 4.74, 4.75, 4.76, 4.7700000000000005, 4.78, 4.79, 4.8, 4.8100000000000005, 4.82, 4.83, 4.84, 4.8500000000000005, 4.86, 4.87, 4.88, 4.89, 4.9, 4.91, 4.92, 4.93, 4.94, 4.95, 4.96, 4.97, 4.98, 4.99, 5.0, 5.01, 5.0200000000000005, 5.03, 5.04, 5.05, 5.0600000000000005, 5.07, 5.08, 5.09, 5.1000000000000005, 5.11, 5.12, 5.13, 5.14, 5.15, 5.16, 5.17, 5.18, 5.19, 5.2, 5.21, 5.22, 5.23, 5.24, 5.25, 5.26, 5.2700000000000005, 5.28, 5.29, 5.3, 5.3100000000000005, 5.32, 5.33, 5.34, 5.3500000000000005, 5.36, 5.37, 5.38, 5.39, 5.4, 5.41, 5.42, 5.43, 5.44, 5.45, 5.46, 5.47, 5.48, 5.49, 5.5, 5.51, 5.5200000000000005, 5.53, 5.54, 5.55, 5.5600000000000005, 5.57, 5.58, 5.59, 5.6000000000000005, 5.61, 5.62, 5.63, 5.64, 5.65, 5.66, 5.67, 5.68, 5.69, 5.7, 5.71, 5.72, 5.73, 5.74, 5.75, 5.76, 5.7700000000000005, 5.78, 5.79, 5.8, 5.8100000000000005, 5.82, 5.83, 5.84, 5.8500000000000005, 5.86, 5.87, 5.88, 5.89, 5.9, 5.91, 5.92, 5.93, 5.94, 5.95, 5.96, 5.97, 5.98, 5.99, 6.0, 6.01, 6.0200000000000005, 6.03, 6.04, 6.05, 6.0600000000000005, 6.07, 6.08, 6.09, 6.1000000000000005, 6.11, 6.12, 6.13, 6.140000000000001, 6.15, 6.16, 6.17, 6.18, 6.19, 6.2, 6.21, 6.22, 6.23, 6.24, 6.25, 6.26, 6.2700000000000005, 6.28, 6.29, 6.3, 6.3100000000000005, 6.32, 6.33, 6.34, 6.3500000000000005, 6.36, 6.37, 6.38, 6.390000000000001, 6.4, 6.41, 6.42, 6.43, 6.44, 6.45, 6.46, 6.47, 6.48, 6.49, 6.5, 6.51, 6.5200000000000005, 6.53, 6.54, 6.55, 6.5600000000000005, 6.57, 6.58, 6.59, 6.6000000000000005, 6.61, 6.62, 6.63, 6.640000000000001, 6.65, 6.66, 6.67, 6.68, 6.69, 6.7, 6.71, 6.72, 6.73, 6.74, 6.75, 6.76, 6.7700000000000005, 6.78, 6.79, 6.8, 6.8100000000000005, 6.82, 6.83, 6.84, 6.8500000000000005, 6.86, 6.87, 6.88, 6.890000000000001, 6.9, 6.91, 6.92, 6.93, 6.94, 6.95, 6.96, 6.97, 6.98, 6.99, 7.0, 7.01, 7.0200000000000005, 7.03, 7.04, 7.05, 7.0600000000000005, 7.07, 7.08, 7.09, 7.1000000000000005, 7.11, 7.12, 7.13, 7.140000000000001, 7.15, 7.16, 7.17, 7.18, 7.19, 7.2, 7.21, 7.22, 7.23, 7.24, 7.25, 7.26, 7.2700000000000005, 7.28, 7.29, 7.3, 7.3100000000000005, 7.32, 7.33, 7.34, 7.3500000000000005, 7.36, 7.37, 7.38, 7.390000000000001, 7.4, 7.41, 7.42, 7.43, 7.44, 7.45, 7.46, 7.47, 7.48, 7.49, 7.5, 7.51, 7.5200000000000005, 7.53, 7.54, 7.55, 7.5600000000000005, 7.57, 7.58, 7.59, 7.6000000000000005, 7.61, 7.62, 7.63, 7.640000000000001, 7.65, 7.66, 7.67, 7.68, 7.69, 7.7, 7.71, 7.72, 7.73, 7.74, 7.75, 7.76, 7.7700000000000005, 7.78, 7.79, 7.8, 7.8100000000000005, 7.82, 7.83, 7.84, 7.8500000000000005, 7.86, 7.87, 7.88, 7.890000000000001, 7.9, 7.91, 7.92, 7.930000000000001, 7.94, 7.95, 7.96, 7.97, 7.98, 7.99, 8.0, 8.01, 8.02, 8.03, 8.040000000000001, 8.05, 8.06, 8.07, 8.08, 8.09, 8.1, 8.11, 8.120000000000001, 8.13, 8.14, 8.15, 8.16, 8.17, 8.18, 8.19, 8.2, 8.21, 8.22, 8.23, 8.24, 8.25, 8.26, 8.27, 8.28, 8.290000000000001, 8.3, 8.31, 8.32, 8.33, 8.34, 8.35, 8.36, 8.370000000000001, 8.38, 8.39, 8.4, 8.41, 8.42, 8.43, 8.44, 8.45, 8.46, 8.47, 8.48, 8.49, 8.5, 8.51, 8.52, 8.53, 8.540000000000001, 8.55, 8.56, 8.57, 8.58, 8.59, 8.6, 8.61, 8.620000000000001, 8.63, 8.64, 8.65, 8.66, 8.67, 8.68, 8.69, 8.700000000000001, 8.71, 8.72, 8.73, 8.74, 8.75, 8.76, 8.77, 8.78, 8.790000000000001, 8.8, 8.81, 8.82, 8.83, 8.84, 8.85, 8.86, 8.870000000000001, 8.88, 8.89, 8.9, 8.91, 8.92, 8.93, 8.94, 8.950000000000001, 8.96, 8.97, 8.98, 8.99]

正如您所看到的，有些数字并不是您所期望的：

8.540000000000001
8.620000000000001
8.700000000000001
8.790000000000001

等等。您应该做的只是更改lambda函数：

>>> # Looking at your range, you seem to want to include 0.01 and 8.99
>>> filtered = filter(lambda x: x>=0.01 and x<=8.99, reader)

您还应注意，如果要查看列表中是否有数字，则应在

中使用

，而不是
=
：

>>> # Assuming `low` is a working list
>>> filtered = filter(lambda p: p in low, reader)

编辑

由于您使用的是
csv.reader
，看起来您还需要一些步骤。“更脏”的方法是将
读卡器
扁平化为一个列表：

reader.next() #Remove the header 
l = [float(x[0]) for x in reader]

使用它：

filtered = filter(lambda x: x>=0.01 and x<=8.99, l)

这一行是错误的：

filtered = filter(lambda p:low == p[0], reader) 

low
是一个
列表
p[0]
是一个
浮点数
。lambda表达式永远不会是
True
，因为没有
list
可以等于
float

尝试：

filtered=filter（λp:0.01此行错误：

filtered = filter(lambda p:low == p[0], reader) 

low
是一个
list
p[0]
是一个
float
。lambda表达式永远不会
True
，因为没有
list
可以等于
float

尝试：

filtered=filter（lambda p:0.01我想他在low中的意思是
p
@MoonCheesez-OP的标准-
…在定义的范围内…
@MoonCheesez-我没有想到，但我相信你是对的。这就是OP试图表达的。不过，这有它自己的一套错误。@wwii查看他的值，最大的小数点是2。我想k这就是他这样做的原因。我想他只是指
p在low
@MoonCheesez-OP的标准-
…在一个定义的范围内…
@MoonCheesez-我没有想到，但我相信你是对的。这就是OP试图表达的。不过，这有它自己的一套错误。@wwii查看他的值，最大的十进制p蕾丝2岁。我想这就是他那样做的原因。谢谢！这帮了大忙。
p[0]
我是不是只想从
reader
中筛选第一列。在实际的csv中，我需要它根据第五列筛选行，但我不确定应该将其放在lambda中的什么位置？@iOSecure忘了您使用的是
csv.reader
。请参阅我最近的编辑。谢谢！这帮了大忙。
p[0]
我是不是只想从
reader
中筛选第一列。在实际的csv中，我需要它根据第五列筛选行，但我不确定应该将其放在lambda中的什么位置？@iOSecure忘记了您使用的是
csv.reader
。请参阅我最近的编辑。