당신은 주제를 찾고 있습니까 “cannot convert the series to class float – วิธีแก้ไข Error \”cannot convert the series to class ‘float’ \”“? 다음 카테고리의 웹사이트 Chewathai27.com/you 에서 귀하의 모든 질문에 답변해 드립니다: Chewathai27.com/you/blog. 바로 아래에서 답을 찾을 수 있습니다. 작성자 I – Code Journey 이(가) 작성한 기사에는 조회수 332회 및 좋아요 3개 개의 좋아요가 있습니다.
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TypeError: cannot convert the series to
You can use numpy.log instead. Math.log is expecting a single number, not array.
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Date Published: 5/8/2021
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TypeError: cannot convert the series to – Coding Discuss
TypeError: cannot convert the series to < 'float'>. I have a dataframe (df) that looks like: date A 2001-01-02 1.0022 2001-01-03 1.1033 …
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Date Published: 12/6/2022
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TypeError: cannot convert the series to
My data is in a data frame and I keep getting this error (TypeError: cannot convert the series to < 'float'>). This is a view of my data.
Source: community.influxdata.com
Date Published: 6/9/2022
View: 964
“dataframe apply cannot convert series to class ‘float’” Code …
“dataframe apply cannot convert series to ‘float’” Code Answer. how to convert a pandas series from int to float in python.
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Typeerror: cannot convert the series to [Step-by-step Guide]
convert object to pandas floats; pandas string …
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python – TypeError: cannot convert the series to
You can use numpy.log instead. Math.log is expecting a single number, not array.
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cannot convert the series to (converter) – Fix Exception
cannot convert the series to (converter). Package: pandas logo pandas. github stars 30911. Exception Class: TypeError …
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주제와 관련된 더 많은 사진을 참조하십시오 วิธีแก้ไข Error \”cannot convert the series to class ‘float’ \”. 댓글에서 더 많은 관련 이미지를 보거나 필요한 경우 더 많은 관련 기사를 볼 수 있습니다.
주제에 대한 기사 평가 cannot convert the series to class float
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How do you convert a series to a float in Python?
Alternatively, you can convert all string columns to float type using pandas. to_numeric() . For example use df[‘Discount’] = pd. to_numeric(df[‘Discount’]) function to convert ‘Discount’ column to float.
How do pandas change objects to float?
- Use the astype() Method to Convert Object to Float in Pandas.
- Use the to_numeric() Function to Convert Object to Float in Pandas.
How do you convert a series to a DataFrame in Python?
to_frame() function is used to convert the given series object to a dataframe. Parameter : name : The passed name should substitute for the series name (if it has one).
How do you turn a series into a DataFrame?
…
Syntax
- The passed name should substitute for the series name (if it has one).
- The fault is None.
- Returns the DataFrame representation of Series.
How do you change the data type of a series?
Change data type of a series in Pandas
Use a numpy. dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, …}, where col is a column label and dtype is a numpy. dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.
How do I change an object’s datatype to float in Python?
- You can use pandas.Series.astype.
- You can do something like this : weather[“Temp”] = weather.Temp.astype(float)
- You can also use pd.to_numeric that will convert the column from object to float.
How do you convert a string to a float?
We can convert a string to float in Python using float() function. It’s a built-in function to convert an object to floating point number. Internally float() function calls specified object __float__() function.
How do you change the series of a object in Python?
Change data type of a series in Pandas
Use a numpy. dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, …}, where col is a column label and dtype is a numpy. dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.
How do you convert a series to an int in Python?
- astype(str) # Transform as character.
- astype(float) # Transform as float.
- astype(int) # Transform as numeric.
How do you convert a string to a float in Python?
We can convert a string to float in Python using float() function. It’s a built-in function to convert an object to floating point number. Internally float() function calls specified object __float__() function.
How do you convert a float to an int in Python?
Python also has a built-in function to convert floats to integers: int() . In this case, 390.8 will be converted to 390 . When converting floats to integers with the int() function, Python cuts off the decimal and remaining numbers of a float to create an integer.
TypeError: cannot convert the series to
I have a dataframe (df) that looks like:
date A 2001-01-02 1.0022 2001-01-03 1.1033 2001-01-04 1.1496 2001-01-05 1.1033 2015-03-30 126.3700 2015-03-31 124.4300 2015-04-01 124.2500 2015-04-02 124.8900
For the entire time-series I’m trying to divide today’s value by yesterdays and log the result using the following:
df[“B”] = math.log(df[“A”] / df[“A”].shift(1))
However I get the following error:
TypeError: cannot convert the series to
How can I fix this? I’ve tried to cast as float using:
df[“B”] .astype(float)
But can’t get anything to work.
TypeError: cannot convert the series to
TypeError: cannot convert the series to
I have a dataframe (df) that looks like:
date A 2001 -01-02 1.0022 2001 -01-03 1.1033 2001 -01-04 1.1496 2001 -01-05 1.1033 2015 -03- 30 126.3700 2015 -03- 31 124.4300 2015 -04-01 124.2500 2015 -04-02 124.8900
For the entire time-series I’m trying to divide today’s value by yesterdays and log the result using the following:
df[ “B” ] = math.log(df[ “A” ] / df[ “A” ].shift( 1 ))
However I get the following error:
TypeError: cannot convert the series to < class ' float '>
How can I fix this? I’ve tried to cast as float using:
df[ “B” ] .astype( float )
But can’t get anything to work.
TypeError: cannot convert the series to
Hi there,
Having a bit of challenge ingesting data into influxdb v2. My data is in a data frame and I keep getting this error (TypeError: cannot convert the series to
). This is a view of my data
RangeIndex: 500 entries, 0 to 499 Data columns (total 6 columns): # Column Non-Null Count Dtype — —— ————– —– 0 timestamp 500 non-null int64 1 open 500 non-null float64 2 high 500 non-null float64 3 low 500 non-null float64 4 close 500 non-null float64 5 volume 500 non-null float64 dtypes: float64(5), int64(1) memory usage: 23.6 KB I created a Point object as follows
def parse_data(df): return Point(“crypto_ohlcv”) \ .tag(“exchange”, “binance”) \ .field(“open”, df[‘open’].astype(float)) \ .field(“high”, df[‘high’].astype(float)) \ .field(“low”, df[‘low’].astype(float)) \ .field(“close”, df[‘close’].astype(float)) \ .field(“volume”, df[‘volume’].astype(float))\ .time(df.timestamp)
and trying to injest the data as follows
write_api.write(bucket=bucket, org=org, record=parse_data(data))
Any suggestions on what could be the issue will be helpful
Pandas Convert Column to Float in DataFrame
By using pandas DataFrame.astype() and pandas.to_numeric() methods you can convert a column from string/int type to float. In this article, I will explain how to convert one or multiple string columns to float type using examples.
1. Quick Examples of Convert String to Float in DataFrame
If you are in a hurry, below are some quick examples of how to convert string to float. You can apply these to convert from any type in Pandas
# Below are quick example # convert “Fee” from string to float df[‘Fee’] = df[‘Fee’].astype(float) print(df.dtypes) # Convert multiple columns df = df.astype({‘Fee’:’float’,’Discount’:’float’}) # Convert all columns to floats df = df.astype(float) print(df.dtypes) # Convert numeric function string to float df[‘Discount’] = pd.to_numeric(df[‘Discount’]) print(df.dtypes) # Convert DataFrame column from string to float df[“Discount”] = pd.to_numeric(df[“Discount”], downcast=”float”) print(df.dtypes) # Convert each value of the column to a string df[‘Discount’] = pd.to_numeric(df[‘Discount’], errors=’coerce’) print(df.dtypes) # Using df.replace() to replace nan values 0 before convertion df[‘Discount’] = pd.to_numeric(df[‘Discount’], errors=’coerce’) df = df.replace(np.nan, 0, regex=True) print(df) print(df.dtypes) # Replace empty string (”) with np.nan before convertion df[‘Discount’]=df.Discount.replace(”,np.nan).astype(float) print(df) print(df.dtypes)
Now, let’s create a DataFrame with a few rows and columns, execute the above examples and validate the results. Our DataFrame contains column names Fee and Discount .
import pandas as pd import numpy as np technologies= ({ ‘Fee’ :[‘22000.30′,’25000.40′,’23000.20′,’24000.50′,’26000.10’], ‘Discount’:[‘1000.10′,np.nan,’1000.5′,np.nan,’2500.20’] }) df = pd.DataFrame(technologies) print(df.dtypes)
You can identify the data type of each column by using dtypes . For Instance, print(df.dtypes) outputs as below. Here object means a String type.
Fee object Discount object dtype: object
2. pandas Convert String to Float
Use pandas DataFrame.astype() function to convert column from string/int to float, you can apply this on a specific column or on an entire DataFrame. To cast the data type to 54-bit signed float, you can use numpy.float64 , numpy.float_ , float , float64 as param. To cast to 32-bit signed float, use numpy.float32 or float32 .
The Below example converts Fee column from string dtype to float64 .
# Convert “Fee” from string to float df = df.astype({‘Fee’:’float’}) print(df.dtypes)
Yields below output.
Fee float64 Discount object dtype: object
You can also use Series.astype() to convert a specific column. since each column on DataFrame is pandas Series, I will get the column from DataFrame as Series and use astype() function. In the below example df.Fee or df[‘Fee’] returns Series object.
# Convert “Fee” from string to float df[‘Fee’] = df[‘Fee’].astype(float) print(df.dtypes)
Yields same output as above.
3. Convert Multiple Columns to Float
You can also convert multiple columns to float by sending dict of column name -> data type to astype() method. The below example converts column Fee and Discount to float dtype.
# Convert multiple columns df = df.astype({‘Fee’:’float’,’Discount’:’float’})
4. Convert All Columns to Float Type
By default astype() function converts all columns to the same type. The below example converts all DataFrame columns to float type. If you have any column with alpha-numeric values, you will get an error.
# Convert entire DataFrame string to float df = df.astype(float) print(df.dtypes)
Yields below output.
Fee float64 Discount float64 dtype: object
5. Using pandas.to_numeric()
Alternatively, you can convert all string columns to float type using pandas.to_numeric() . For example use df[‘Discount’] = pd.to_numeric(df[‘Discount’]) function to convert ‘Discount’ column to float.
# Convert numeric function string to float df[‘Discount’] = pd.to_numeric(df[‘Discount’]) print(df.dtypes) # Convert DataFrame column from string to float df[“Discount”] = pd.to_numeric(df[“Discount”], downcast=”float”) print(df.dtypes)
Yields below output.
Fee object Discount float64 dtype: object
6. Handling Non-numeric Values
When you have some cells with character values on a column you wanted to convert to float, it returns an error. To ignore the error and convert the char values to NaN use errors=’coerce’ attribute.
# Convert each value of the column to a string df[‘Discount’] = pd.to_numeric(df[‘Discount’], errors=’coerce’) print(df.dtypes)
This yields the same output as above.
7. Replace the ‘NaN’ Values with Zeros
Use df=df.replace(np.nan,0,regex=True) function to replace the ‘NaN’ values with ‘0’ values.
# Using df.replace() to replace nan values 0 df[‘Discount’] = pd.to_numeric(df[‘Discount’], errors=’coerce’) df = df.replace(np.nan, 0, regex=True) print(df) print(df.dtypes)
Yields below output.
Fee Discount 0 22000.30 1000.1 1 25000.40 0.0 2 23000.20 1000.5 3 24000.50 0.0 4 26000.10 2500.2 Fee object Discount float64 dtype: object
8. Replace Empty String before Convert
If you have empty values in a string, convert empty string (”) with np.nan before converting it to float.
import pandas as pd import numpy as np technologies= ({ ‘Fee’ :[‘22000.30′,’25000.40′,’23000.20′,’24000.50′,’26000.10′,’21000’], ‘Discount’:[‘1000.10′,np.nan,””,np.nan,’2500.20’,””] }) df = pd.DataFrame(technologies) # Replace empty string (”) with np.nan df[‘Discount’]=df.Discount.replace(”,np.nan).astype(float) print(df) print(df.dtypes)
Yields below output.
Fee Discount 0 22000.30 1000.1 1 25000.40 NaN 2 23000.20 NaN 3 24000.50 NaN 4 26000.10 2500.2 5 21000 NaN Fee object Discount float64 dtype: object
Conclusion
In this article, you have learned how to convert single, multiple, and all columns from string type to float in Pandas DataFrame using DataFrame.astype(float) and pandas.to_numeric() function.
Happy Learning !!
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References
Convert Object to Float in Pandas
In this tutorial, we will focus on converting an object-type column to float in Pandas. An object-type column contains a string or a mix of other types, whereas float contains decimal values. We will work on the following DataFrame in this article.
import pandas as pd df = pd.DataFrame([[‘10.0’ ,6,7,8], [‘1.0’ ,9,12,14], [‘5.0’ ,8,10,6]], columns = [‘a’,’b’,’c’,’d’]) print(df) print(“—————————“) print(df.info())
Output:
a b c d 0 10.0 6 7 8 1 1.0 9 12 14 2 5.0 8 10 6 —————————
RangeIndex: 3 entries, 0 to 2 Data columns (total 4 columns): # Column Non-Null Count Dtype — —— ————– —– 0 a 3 non-null object 1 b 3 non-null int64 2 c 3 non-null int64 3 d 3 non-null int64 dtypes: int64(3), object(1) memory usage: 224.0+ bytes None Notice the type of column ‘a’ , which is of the object type. We will convert this object to float using pd.to_numeric() and astype() functions in Pandas.
Note This tutorial won’t cover convert_objects() function which is deprecated and removed.
Use the astype() Method to Convert Object to Float in Pandas
Pandas provide the astype() method to convert a column to a specific type. We pass float to the method and set the parameter errors as ‘raise’ , which means it will raise exceptions for invalid values. Example:
import pandas as pd df = pd.DataFrame([[‘10.0’ ,6,7,8], [‘1.0’ ,9,12,14], [‘5.0’ ,8,10,6]], columns = [‘a’,’b’,’c’,’d’]) df[‘a’] = df[‘a’].astype(float, errors = ‘raise’) print(df.info())
Output:
RangeIndex: 3 entries, 0 to 2 Data columns (total 4 columns): a 3 non-null float64 b 3 non-null int64 c 3 non-null int64 d 3 non-null int64 dtypes: float64(1), int64(3) memory usage: 224.0 bytes Use the to_numeric() Function to Convert Object to Float in Pandas
The Pandas to_numeric() function can be used to convert a list, a series, an array, or a tuple to a numeric datatype, which means signed, or unsigned int and float type. It also has the errors parameter to raise exceptions. An example of converting the object type to float using to_numeric() is shown below:
import pandas as pd df = pd.DataFrame([[‘10.0’ ,6,7,8], [‘1.0’ ,9,12,14], [‘5.0’ ,8,10,6]], columns = [‘a’,’b’,’c’,’d’]) df[‘a’] = pd.to_numeric(df[‘a’],errors = ‘coerce’) print(df.info())
Output:
RangeIndex: 3 entries, 0 to 2 Data columns (total 4 columns): a 3 non-null float64 b 3 non-null int64 c 3 non-null int64 d 3 non-null int64 dtypes: float64(1), int64(3) memory usage: 224.0 bytes
Typeerror: cannot convert the series to [Step-by-step Guide]
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“Applicable dataframe cannot convert string to class ‘float’” Code Answer
how to convert a pandas string from int to float in pythonpython by Dull Dove on March 4, 20201
xxxxxxxxxx
1
df[‘DataFrame Column’] = df[‘DataFrame Column’]. astype (float)
2
Source: datatofish.comAdd Grepper answer
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Ask QuestionAsked 5 years agoModified 12 months agoViewed 139k times
I have a dataframe (df) that looks like:
date A2001-01-02 1.00222001-01-03 1.10332001-01-04 1.14962001-01-05 1.10332015-03-30 126.37002015-03-31 124.43002015-04-01 124.25002015-04-02 124.8900
For the entire time-series I’m trying to divide today’s value by yesterdays and log the result using the following:
df[“B”] = math.log(df[“A”] / df[“A”].shift(1))
However I get the following error:
TypeError: cannot convert the series to
How can I fix this? I’ve tried to cast as float using:
df[“B”] .astype(float)
But can’t get anything to work.python python-3.xFollowFollow this question to receive notifications45 AnswersSorted by: Reset to default Highest score (default) Date modified (newest first) Date created (oldest first)
You can use numpy.log instead. Math.log is expecting a single number, not array.Follow
You can use lambda operator to apply your functions to the pandas data frame or to the series. More specifically if you want to convert each element on a column to a floating point number, you should do it like this:
df[‘A’].apply(lambda x: float(x))
here the lambda operator will take the values on that column (as x) and return them back as a float value.Follow2
If you just write df[“A”].astype(float) you will not change df . You would need to assign the output of the astype method call to something else, including to the existing series using df[‘A’] = df[‘A’].astype(float) . Also you might want to either use numpy as @user3582076 suggests, or use .apply on the Series that results from dividing today’s value by yesterday’s.Follow
J’ai eu le même problème, pour moi la réponse était de regarder la raison pour laquelle j’avais des séries en premier lieu. Après avoir longuement cherché comment changer la série en différents types de données assignés, j’ai réalisé que j’avais défini deux fois le même nom de colonne dans la trame de données et c’est pourquoi j’avais une série.
Removing accidental column name duplication removes this issue :)Follow1
I used in a different way but it’s the same as @cemosambora
(df.A).apply(lambda x: float(x)) Here df is the pandas dataframe and A is a column nameFollow this answer to get notifications Your answer
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TypeError: cannot convert the series to
I have a dataframe (df) that looks like:
date A 2001-01-02 1.0022 2001-01-03 1.1033 2001-01-04 1.1496 2001-01-05 1.1033 2015-03-30 126.3700 2015-03-31 124.4300 2015-04-01 124.2500 2015-04-02 124.8900
For the entire time-series I’m trying to divide today’s value by yesterdays and log the result using the following:
df[“B”] = math.log(df[“A”] / df[“A”].shift(1))
However I get the following error:
TypeError: cannot convert the series to
How can I fix this? I’ve tried to cast as float using:
df[“B”] .astype(float)
But can’t get anything to work.
See Question&Answers more detail: os
[Fixed] cannot convert the series to (converter)
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키워드에 대한 정보 cannot convert the series to class float
다음은 Bing에서 cannot convert the series to class float 주제에 대한 검색 결과입니다. 필요한 경우 더 읽을 수 있습니다.
이 기사는 인터넷의 다양한 출처에서 편집되었습니다. 이 기사가 유용했기를 바랍니다. 이 기사가 유용하다고 생각되면 공유하십시오. 매우 감사합니다!
사람들이 주제에 대해 자주 검색하는 키워드 วิธีแก้ไข Error \”cannot convert the series to class ‘float’ \”
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วิธีแก้ไข #Error #\”cannot #convert #the #series #to #class #’float’ #\”
YouTube에서 cannot convert the series to class float 주제의 다른 동영상 보기
주제에 대한 기사를 시청해 주셔서 감사합니다 วิธีแก้ไข Error \”cannot convert the series to class ‘float’ \” | cannot convert the series to class float, 이 기사가 유용하다고 생각되면 공유하십시오, 매우 감사합니다.
