我使用下面的代码对文本进行评级
import textstat
import pandas as pd
test_data = ("""Jonathan pushed back the big iron pot and stood up.
There were no bears. But up the path came his father, carrying his gun. And with
him were Jonathan's Uncle James and his Uncle Samuel, his Uncle John and his
Uncle
我正在尝试计算pandas系列中的NaN元素(数据类型类‘numpy.float64’),以了解有多少个数据类型是类'pandas.core.series.Series‘
这是为了计数pandas系列中的空值
import pandas as pd
oc=pd.read_csv(csv_file)
oc.count("NaN")
我期望oc,count("NaN")的输出是7,但它显示的是'Level NaN must be same as name (None)'
我想在Pandas中添加两个系列,并使用add()函数这样做:
import pandas as pd
import numpy as np
a = pd.Series([35000,71000,16000,5000],index=['Ohio','Texas','Oregon','Utah'])
b = pd.Series([np.nan,71000,16000,35000],index=['California', 'Texas', 'Oregon', 'Ohio'
使用以下csv数据:
我已经将数据从csv加载到Pandas Pivot Table中,输出如下:
[[nan nan nan ... nan nan 0.]
[nan 21 nan ... nan 0. nan]
[nan nan nan ... 0. nan nan]
...
[23. nan 13. ... nan nan nan]
[nan nan nan ... nan nan nan]
[nan 14 nan ... nan nan nan]]
但在对结果使用Scipy高斯滤波器后,数据随后被消隐,如下所示:
[[nan nan nan ... nan nan
Hi有一个数据框架(df),其中包含两个列(日期、文本),从Excel电子表格中读取到Python/Pandas。
xl = pd.ExcelFile(dir+"file.xlsx")
df = xl.parse(xl.sheet_names[0])
date text
0 2013-08-06 NaN
1 2013-08-06 Text with unicode
2 ...
文本包含不需要的unicode字符,我通常使用
df['text'] = df[&
我想挤一个这样的数据文件:
import pandas as pd
import numpy as np
df1 = pd.DataFrame([[1,pd.NA,100],[2,20,np.nan],[np.nan,np.nan,300],[pd.NA,"bla",400]], columns=["A","B","C"])
df1
A B C
0 1 <NA> 100.0
1 2 20 NaN
2 NaN NaN 300.0
3 <NA
我正努力把南安移除。已经花了一些时间来寻找解决方案,但似乎没有任何效果。
下面是我的代码示例。整个笔记本都可以在我的GitHub上找到:
import pandas as pd
import seaborn as sns #not used in this sample, needed for plotting later on
import matplotlib as mpl #as above
import matplotlib.pyplot as plt #as above
import numpy as np
我为分层聚类编写了以下代码,但我得到了以下错误,您能帮助我吗?
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the Mall dataset with pandas
dataset =
pd.read_csv("https://raw.githubusercontent.com/akbarhusnoo/Chronic-Kidney-Disease-Prediction/main/chronic_kidne
假设我有一个大的数据集,并且我想要在一个长时间内应用一个滚动操作,但是我只想在少量的数据点上应用聚合。我能用pandas做这个吗?
当我试图将切片程序应用于聚合函数的结果时,似乎为时已晚,整个计算已经发生:
small = 10
big = 1000
bigger = 10000000
s = pd.Series(np.arange(bigger))
%time x = s.rolling(big).mean()
%time x = s.rolling(big).mean()[:-small]
这方面的产出如下:
CPU times: user 306 ms, sys: 162 ms, to
我正在尝试删除作为nan导入的字典条目(因为它们在excel文件中是空的)。
import pandas as pd
import pprint
from math import isnan
df = pd.read_excel (r'C:\Users\User1\Desktop\Data.xlsx')
d = df.to_dict()
clean = {k: d[k] for k in d if not isnan(k)}
pprint.pprint(clean)
然而,这给了我错误。
TypeError:必须是实数,而不是str
如果我使用下面的命令筛选出nan