Converting Strings to Floats in a Dataframe

Converting string to float - python

The solution that worked in my case was to add an argument errors='coerce' to the to_numeric function.

df["mycolumn"] = pd.to_numeric(df["mycolumn"], errors='coerce', downcast="float")

Converting string to float in pandas

Try replace with regex=True option:

new_df = (new_df[['Eleven', 'Twelve', 'Thirteen', 'Fourteen']]
               .replace(',', '', regex=True)
               .astype(float)
         )

Or use apply:

new_df = (new_df[['Eleven', 'Twelve', 'Thirteen', 'Fourteen']]
               .apply(lambda x: x.str.replace(',', ''))
               .astype(float)
         )

Converting a pandas dataframe column values from string to float

Try this:

df["C"].str.replace(",", "").astype(float)

Convert strings to float in all pandas columns, where this is possible

I think you need parameter errors='ignore' in to_numeric:

df = df.apply(pd.to_numeric, errors='ignore')
print (df.dtypes)
A     object
B      int64
C    float64
dtype: object

It working nice if not mixed values - numeric with strings:

df_list = [["a", "t", "2"], ["b", "3", np.nan]]
df = pd.DataFrame(df_list, columns = list("ABC"))

df = df.apply(pd.to_numeric, errors='ignore')
print (df)
   A  B    C
0  a  t  2.0 <=added t to column B for mixed values
1  b  3  NaN

print (df.dtypes)
A     object
B     object
C    float64
dtype: object

EDIT:

You can downcast also int to floats:

df = df.apply(pd.to_numeric, errors='ignore', downcast='float')
print (df.dtypes)
A     object
B    float32
C    float32
dtype: object

It is same as:

df = df.apply(lambda x: pd.to_numeric(x, errors='ignore', downcast='float'))
print (df.dtypes)
A     object
B    float32
C    float32
dtype: object

Transform string that should be list of floats in a column of dataframe?

Use ast.literal_eval:

import ast

df['interval'] = df['interval'].apply(ast.literal_eval)

Output

>>> df
       interval
0  [100.0, 3.0]
1    [3.0, 2.0]
2    [2.0, 1.0]
3     [1, 0.25]
4   [0.25, 0.0]

>>> df.loc[0, 'interval']
[100.0, 3.0]

>>> type(df.loc[0, 'interval'])
list

Now you can convert to columns if you want:

>>> df['interval'].apply(pd.Series)
        0     1
0  100.00  3.00
1    3.00  2.00
2    2.00  1.00
3    1.00  0.25
4    0.25  0.00

Convert strings with a - (En Dashes) into a floats

How i thought the string should be made to floats.

import pandas as pd

df = pd.DataFrame(
    {
        "Name": ["alta floresta d'oeste", "ariquemes"],
        "LONGITUDE" : ["-61.999.824", "-1.193.554"],
        "LATITUDE" : ["-63.033.269", "-9.908.463"]
    }
)

def remove_dot(s):
    s = s.split(".")
    x = s[0] + "." + "".join(s[1:])
    return(x)

new_df = pd.concat(
    [
        df["Name"],
        df["LONGITUDE"].apply(remove_dot),
        df["LATITUDE"].apply(remove_dot)
    ],
    axis = 1
)

new_df = new_df.astype({'LONGITUDE':'float','LATITUDE':'float'})

new_df.head()

Gives me this:

None	Name	LONGITUDE	LATITUDE
0	alta floresta d'oeste	-61.999824	-63.033269
1	ariquemes	-1.193554	-9.908463

ValueError: could not convert string to float: 'Null' (Pandas)

Instead of using:

test_dataframe.at[2, 'QTY'] = "Null"

You can use this instead to change the value of any row within any column to "Null"(i.e. In this case I have implemented it to the third row of the third column.

test_dataframe.loc[2, "QTY"] = 'Null'

This code worked well in my case, please do comment if you are facing with any issue:

Sample Image