此问题已在此处有答案：

Efficient way to partially read large numpy file?（1个答案）
How do I read a large csv file with pandas?（16个回答）
上个月关门了。
我试图用python绘制一个大数据（近700万个点）的直方图，我想知道值的频率。我试过这个代码，但它需要太长的时间才能完成一个多小时！有什么建议吗？

import numpy as np
import matplotlib.pyplot as plt

file_path = "D:/results/planarity2.txt" 
data_array = []

with open(file_path, "r") as file:
    for line in file:
        value = line.strip()  
        data_array.append(value)
column_values = data_array 

unique_values, counts = np.unique(column_values, return_counts=True)

value_frequency = dict(zip(unique_values, counts))

x_values = list(value_frequency.keys())
y_values = list(value_frequency.values())

plt.bar(x_values, y_values, edgecolor='black', alpha=0.7)

plt.xlabel('Column Values')
plt.ylabel('Frequency')
plt.title('Frequency of Points Based on Column Values')
plt.show()

我也试过这个，但没有用

import numpy as np
import matplotlib.pyplot as plt

file_path = "D:/results/planarity2.txt" 
data_array = []

with open(file_path, "r") as file:
    for line in file:
        value = line.strip()  
        data_array.append(value)
column_values = data_array 
value_frequency = {}

for value in column_values:
    if value in value_frequency:
        value_frequency[value] += 1
    else:
        value_frequency[value] = 1

x_values = list(value_frequency.keys())
y_values = list(value_frequency.values())

plt.bar(x_values, y_values, edgecolor='black', alpha=0.7)

plt.xlabel('Column Values')
plt.ylabel('Frequency')
plt.title('Frequency of Points Based on Column Values')
plt.show()