NA前面不能有空格，你可以看一下老师的源代码
太牛啦兄弟！！！！we like it
统计某一列的NaN 数据的数量
nan_col_statistics = data.isna().sum()
获取统计结果中最大值的列名
max_col_name = nan_col_statistics.idxmax()
data.drop([max_col_name], axis = 1)
想问一下大家这个问题该如何处理
import os
os.makedirs(os.path.join(‘…’, ‘data’), exist_ok=True)
Traceback (most recent call last):
File “”, line 1, in
File “C:\conda\envs\d2l\lib\os.py”, line 223, in makedirs
mkdir(name, mode)
PermissionError: [WinError 5] 拒绝访问。: ‘…\data’
data_file = os.path.join(‘…’, ‘data’, ‘house_tiny.csv’)
with open(data_file, ‘w’) as f:
… f.write(‘NumRooms,Alley,Price\n’) # 列名
… f.write(‘NA,Pave,127500\n’) # 每行表示一个数据样本
… f.write(‘2,NA,106000\n’)
… f.write(‘4,NA,178100\n’)
… f.write(‘NA,NA,140000\n’)
…
Traceback (most recent call last):
File “”, line 1, in
FileNotFoundError: [Errno 2] No such file or directory: ‘…\data\house_tiny.csv’
各位大佬,请问下面的代码为何没能像书中的例子那样将Name列自动转换成两列(Name_Tom, Name_nan)?
os.makedirs(os.path.join('.','data'),exist_ok=True)
datafile = os.path.join('.','data','house_tiny.csv')
with open(datafile,'w') as f:
f.write('NumRoom,Name,Price\n')
f.write('12,Tom,223\n')
f.write('NA,NA,276\n')
f.write('81,NA,654\n')
f.write('92,NA,NA\n')
f.write('76,NA,128\n')
input, output = data.iloc[:,0:2], data.iloc[:,2]
input = input.fillna(input.mean())
output = output.fillna(output.mean())
inputs = pd.get_dummies(input,dummy_na=True)
print(input)
-------运行结果--------
NumRoom Name
0 12.00 Tom
1 65.25 NaN
2 81.00 NaN
3 92.00 NaN
4 76.00 NaN
\data\house_tiny.csv前面应该是两个点
你可以这样运行一下试试，我调整了你的变量命名，我觉得可能的原因是本身input是已被命名的函数名
os.makedirs(os.path.join(’.’,‘data’),exist_ok=True)
datafile = os.path.join(’.’,‘data’,‘house_tiny.csv’)
with open(datafile,‘w’) as f:
f.write(‘NumRoom,Name,Price\n’)
f.write(‘12,Tom,223\n’)
f.write(‘NA,NA,276\n’)
f.write(‘81,NA,654\n’)
f.write(‘92,NA,NA\n’)
f.write(‘76,NA,128\n’)
inputs, outputs = data.iloc[:,0:2], data.iloc[:,2]
inputs = inputs.fillna(input.mean())
outputs = output.fillna(output.mean())
inputs = pd.get_dummies(input,dummy_na=True)
print(inputs)
谢谢.
发现我打印的是原始的input,应该是inputs.
inputs = pd.get_dummies(input,dummy_na=True)的计算结果代入的是inputs
为什么在pandas这章，最后一段代码中X, y = torch.tensor(inputs.values), torch.tensor(outputs.values)这里X会提示数据类型为float64，而如果直接创建torch.tensor([1.0,2.0])，则是float32类型？
交作业
def delete_max_na(data):
null_count_ser = data.isna().sum()
data_pd = data.dropna(axis=1,thresh=data.shape[0]-null_count_ser.max()+1)
data_np = np.array(data_pd)
return torch.tensor(data_np)
交作业，干劲满满
题一：
# 我创建的文件
import os
import torch
os.makedirs(os.path.join('..', 'data'), exist_ok=True)
data_file2 = os.path.join('..', 'data', 'house_tiny.csv')
with open(data_file2, 'w') as f:
f.write('name,age,grade,number\n') # 列名
f.write('li,12,NaN,123\n') # 每行表示一个数据样本
f.write('wang,11,NaN,234\n')
f.write('liao,NaN,NaN,NaN\n')
f.write('zhou,15,9,NaN\n')
#读取文件
import pandas as pd
from pandas import DataFrame as df
data2 = pd.read_csv(data_file2)
print(data2)
#查看每一列的缺失值的数量
data2.shape[1]
nan_num = data2.isnull().sum()
nan_num
#找出缺失值最大的列
nan_max_id = nan_num.idxmax()
nan_max_id #运行结果grade
#删除该列
data3 =data2.drop([nan_max_id], axis=1)
题二：
import torch
data3 = pd.get_dummies(data3, dummy_na=True)
print(data3)
z= torch.tensor(data3.values)
z
1 Like
Z = data.iloc[:, 0:2]
print(Z)
max_nan_name = Z.keys()[np.argmax(np.array(Z.isna().sum().values))]
Z.drop(max_nan_name, axis=1, inplace=True)
print(Z)
NumRooms Alley
0 NaN Pave
1 2.0 NaN
2 4.0 NaN
3 NaN NaN
NumRooms
0 NaN
1 2.0
2 4.0
3 NaN
作业：选出NA最多的列并删除
data = pd.read_csv(data_file)
print('pre-',data)
NA_num = sum(data.isna().values)#统计各列NA的个数，生成一维array
data = data.drop(data.columns[np.argmax(NA_num)],axis = 1)#舍去最多NA的列
print('post-',data)
import re
import csv
# 将复制得到的txt数据装换为csv文件
input_PATH = os.path.join('..','data','ONIs.txt')
output_PATH = os.path.join('..','data','ONIs.csv')
# Open the input text file for reading
with open(input_PATH, 'r') as infile:
# Open the output CSV file for writing
with open(output_PATH, 'w', newline='') as outfile:
# Create a CSV writer object
writer = csv.writer(outfile)
# Loop over the input file, Spliting the line by any amount of whitespace writing it to the output CSV file
for line in infile:
writer.writerow(re.split(r'\s+', line.strip()))# line.strip()表示去掉这一行前后的空白，re.split(r'\s+')将一行
# 按照不确定数量的空格分隔开
import random
# 将得到的CSV文件随机100个取Na值（除了表头）
df = pd.read_csv(output_PATH)
for i in range(1,101):
row = random.randint(1,df.shape[0]-1)
col = random.randint(1,df.shape[1]-1)
df.iloc[row,col] = None
df
# 删除缺失值最多的列
col_with_most_nulls = df.isna().sum().idxmax()
print(col_with_most_nulls)
df.drop(col_with_most_nulls, axis=1, inplace=True)
df
# 转换为张量格式
tensors = torch.tensor(df.values)
tensors
按照你的思路查了一下，可以这样
data = data.drop(pd.isna(data).sum(axis=0).idxmax(), axis=1)
1 Like
new_series = data.count()
max = new_series.argmin()
print(max)
for item in new_series.index:
if new_series[item] == max:
data=data.drop(columns=item)
print(data)
