How to remove the special characters in the columns and convert the columns into float data

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I would like to remove the last characters in the column and convert the column into float. The column type is object.

my column like this :

train['latitude'].head()
             -95.369803)
1             -95.369803)
2    -117.07184056399967)
3     -77.86070029399963)
4            -122.419416)
Name: latitude, dtype: object

I tried this code, but getting errors,

 train['latitude'] = train['latitude'].map(lambda x: re.sub(r')', ' ', x)).replace('', np.float64(0)).astype('float64')  

please help me how to convert object to float

   ---------------------------------------------------------------------------
    error                                     Traceback (most recent call last)
    <ipython-input-99-b5cdc7554b0a> in <module>()
    ----> 1 df = train['latitude'].map(lambda x: re.sub(r')', ' ', x)).replace('', np.float64(0)).astype('float64')

    6 frames
    pandas/_libs/lib.pyx in pandas._libs.lib.map_infer()

    /usr/lib/python3.6/sre_parse.py in parse(str, flags, pattern)
        867     if source.next is not None:
        868         assert source.next == ")"
    --> 869         raise source.error("unbalanced parenthesis")
        870 
        871     if flags & SRE_FLAG_DEBUG:

    error: unbalanced parenthesis at position 0

I assume the column name is latitude

You can actually remove the last character from the column value and then convert it to float.

df["latitude"].astype(str).str[:-1].astype(np.float64)

Data pre-processing: A step-by-step guide, df = pd.read_csv(uploaded['data.csv'])## Read a .json file to pandas dataframe We will check the unique entries in such cases to remove them and change the data col , 'are ', uni_val_col)## Convert string datatype to float wherever This changes special characters of certain columns into NaN (Not a� When you know a specific character to remove from the cell value, just use that character as remove_char in the formula Use the formula = SUBSTITUTE (A3,"!","")

Use str.rstrip

Ex:

import pandas as pd

df = pd.DataFrame({'latitude': ["-95.369803)", "-95.369803)", "-117.07184056399967)"]})
df["latitude"] = df["latitude"].str.rstrip(")").astype(float)
print(df)

Output:

     latitude
0  -95.369803
1  -95.369803
2 -117.071841

How to Convert Strings to Floats in Pandas DataFrame, If so, I'll show you two ways to convert strings to floats using pandas. The goal is to convert the values under the 'Price' column into a float. You can then use errors='coerce') df = df.replace(np.nan, 0, regex=True) print (df) print(df.dtypes). If you want to extract coordinates as seperate columns, you will need another split and convert to number. Or you can extract coordinates separately: df['long'] = np.float(df.Geo.str.replace(pattern, r"\1")) df['lat'] = np.float(df.Geo.str.replace(pattern, r"\2"))

You should be able to do this:

train['latitude'] = train['latitude'].astype(str).str[:1].astype(np.float64)

Simplify your Dataset Cleaning with Pandas, As you can guess, we might expect duplicates in some fields. We will cover Why should we remove characters in a dataset full of data? Well, a We're done with this column, we removed the special characters. Note that We will prefer to use floats for the prices, we just need to convert the column type. How to remove all non-alphanumeric characters from a string in MySQL? Java program to remove all the white spaces from a given string; How to remove characters except digits from string in Python? MySQL query to remove special characters from column values? How to escape all special characters for regex in Python? Remove spaces from std::string

You can do:

train['latitude'] = train['latitude'].str.replace(')', '').astype(np.float64)

You will now have all the values as float after removing the last character.

Cleaning Up Currency Data with Pandas, Not surprisingly the Sales column is stored as an object. If we want to clean up the string to remove the extra characters and convert to a float:. You’ll now see that the Price column has been converted into a float: Scenario 2: Numeric and non-numeric values Let’s create a new DataFrame with two columns (the Product and Price columns).

You can try below code:

train['latitude'].str.extract(r'(-\d+.\d+)').astype(float)

pandas.DataFrame.replace — pandas 0.24.2 documentation, to_replace : str, regex, list, dict, Series, int, float, or None For example, {'a': 1, 'b': 'z'} looks for the value 1 in column 'a' and the value 'z' in column 'b' You can treat this as a special case of passing two lists except that you are specifying Note that when replacing multiple bool or datetime64 objects, the data types in the� Also, I have so many other columns in my original file for which I have to use "Derived Column" in between. So, when I just used the data I sent you, it worked. I will try now with all the data and all the columns and "Derived Column" and see how it goes. But thank you very much for all of your help.

pandas.read_table — pandas 1.1.0 documentation, Sniffer . In addition, separators longer than 1 character and different from '\s+' will be interpreted as regular Row number(s) to use as the column names, and the start of the data. Explicitly pass header=0 to be able to replace existing names. If converters are specified, they will be applied INSTEAD of dtype conversion. Convert character column to numeric in pandas python: Method 1. to_numeric() function converts character column (is_promoted) to numeric column as shown below. df1['is_promoted']=pd.to_numeric(df1.is_promoted) df1.dtypes “is_promoted” column is converted from character to numeric (integer).

Literals, A number in exponential notation is always interpreted as floating-point. defines for the following table columns how it interprets the corresponding numeric literals: To encode special characters within a string literal, precede them with the See Impala Date and Time Functions for functions that can convert between a� When data frame is made from a csv file, the columns are imported and data type is set automatically which many times is not what it actually should have. For example, a salary column could be imported as string but to do operations we have to convert it into float. astype() is used to do such data type conversions.

pyspark.sql module — PySpark master documentation, DataFrame A distributed collection of data grouped into named columns. pyspark .sql.Column The data type string format equals to pyspark.sql.types. Remove the temp table from catalog. If the input col is a string, the output is a list of floats . If None is set, the default value is escape character when escape and quote� Convert Data Frame Column to Numeric in R (2 Examples) | Change Factor, Character & Integer In this R tutorial, I’ll explain how to convert a data frame column to numeric in R . No matter if you need to change the class of factors, characters, or integers , this tutorial will show you how to do it.

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