Python 如何将低内存设置为false？

我需要可视化这个数据集。我第一次遇到一个错误，说我有多个数据类型，所以我正试图将

低内存设置为
False
。但是我找不到正确的语法

import numpy as np 
import pandas as pd
import sklearn
import os
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.manifold import TSNE
import io

from google.colab import files
uploaded = files.upload()

train_data = pd.read_csv(io.BytesIO(uploaded['train.csv'], 
low_memory=False))

num_rows = train_data.shape[0]


counter_nan = train_data.isnull().sum()
counter_without_nan = counter_nan[counter_nan == 0]
train_data = train_data[counter_without_nan.keys()]
train_data = train_data.drop({"Team", "DisplayName" , "GameClock" , 
"PossessionTeam" ,"OffensePersonnel" , "DefensePersonnel" , 
"PlayDirection" , "TimeHandoff" , "TimeSnap" , "PlayerHeight" , 
"PlayerBirthDate" , "PlayerCollegeName" , "Position" , "HomeTeamAbbr" , 
"VisitorTeamAbbr" , "Stadium" , "Location", "Turf"},axis = 1)

c = train_data.iloc[:,:-1].values
standard_scalar = StandardScaler()
c_std = standard_scalar.fit_transform(c)

tsne = TSNE(n_components=2, random_state = 0)
c_test_2d = tsne.fit_transform(c_std)

markers = ('s', 'd', 'o', '^', 'v')
color_map = {0:'red', 1:'blue' ,2:'lightgreen',3:'purple', 4:'cyan'}
plt.figure()
for idx, cl in enumerate(np.unique(c_test_2d)):
    plt.scatter(x=c_test_2d[cl,0], y= c_test_2d[cl,1], c=color_map[idx], 
marker=markers[idx], label=cl)
plt.show()

我期望：

train\u data=pd.read\u csv（io.BytesIO（上传['train.csv']，内存不足=False））

要将
低内存设置为
假
欢迎使用StackOverflow

试着换下线

train_data = pd.read_csv(io.BytesIO(uploaded['train.csv'], low_memory=False))

到

您正在将
low\u memory
参数传递给
io.BytesIO
而不是
pd.read\u csv

train_data = pd.read_csv(io.BytesIO(uploaded['train.csv']), low_memory=False)