Replace() Method Not Working on Pandas Dataframe

replace() method not working on Pandas DataFrame

You need to assign back

df = df.replace('white', np.nan)

or pass param inplace=True:

In [50]:
d = {'color' : pd.Series(['white', 'blue', 'orange']),
   'second_color': pd.Series(['white', 'black', 'blue']),
   'value' : pd.Series([1., 2., 3.])}
df = pd.DataFrame(d)
df.replace('white', np.nan, inplace=True)
df

Out[50]:
    color second_color  value
0     NaN          NaN    1.0
1    blue        black    2.0
2  orange         blue    3.0

Most pandas ops return a copy and most have param inplace which is usually defaulted to False

Why is Pandas replace() method not working?

This is bug with dtype='string': pandas-dev/pandas #35977 - BUG: replacement works for object but not string dtype

It should work as expected if you load with dtype=str or dtype='str':

df = pd.read_csv(f_name, dtype=str)
df = df[df.columns.dropna()]
df[cols_int] = df[cols_int].replace({"[\$]": "", "[,]": ""}, regex=True)
df[cols_int] = df[cols_int].astype(int)

Why pandas DataFrame replace method does not work (inplace=True argument is used)

Because .replace('=', '') requires the cell value to be exactly '=' which is obviously not true in your case.

You may instead use it with regex:

df = pd.DataFrame({'a': ['="abc"', '="bcd"'], 'b': ['="uef"', '="hdd"'], 'c':[1,3]})
df.replace([r'^="', r'"$'], '', regex=True, inplace=True)
print(df)

     a    b  c
0  abc  uef  1
1  bcd  hdd  3

Two regular expressions are used here, with the first taking care of the head and the second the tail.

Pandas replace function does not work on Series of strings

To work with text data, you need to use '.str' more info here - https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html.

import pandas as pd
dic = {'Text': ['i8am going to school', 'i8am a very good boy']}
df = pd.DataFrame(dic)
d = df['Text'].str.replace('i8am', 'i am')

Output -

0    i am going to school
1    i am a very good boy

Plus, You need to check the function signature. You can't just pass random keyword args and expect it to work.

My Panda's DataFrame Replace Function is not working

Use a dictionary as reference for replace:

df = pd.DataFrame([['<=50K', '>50K'],
                   ['a', '<=50K']])
#        0      1
# 0  <=50K   >50K
# 1      a  <=50K

df = df.replace({'<=50K': 0, '>50K': 1})

output:

   0  1
0  0  1
1  a  0

If acting on a single columns, use map:

df[0] = df[0].map({'<=50K': 0, '>50K': 1})

To handle missing values with a default:

d = {'<=50K': 0, '>50K': 1}
df = df.applymap(lambda x: d.get(x, -1))

output:

   0  1
0  0  1
1 -1  0

Why the replace method of pandas does not work with None as value?

As was answered here:

df.replace("NAN", None) is equivalent to df.replace("NAN", None, method='pad') if you look into the source code.

Both df.replace({"NAN": None}) and df.replace("NAN", np.nan) give you what you want

Replace method does not work in python pandas

Made some assumptions of your df format but this should work;

import pandas as pd
df = pd.DataFrame({'id': [1, 2, 3], 'date': ['5.30.2021 10:22:00', '5.30.2022 11:45:08', '5.28.2022 10:45:13']})
# replace . with / in date column
df['date'] = df['date'].str.replace('.', '/',regex=False)
print(df)

Output:

   id                date
0   1  5/30/2021 10:22:00
1   2  5/30/2022 11:45:08
2   3  5/28/2022 10:45:13

Pandas DataFrame replace does not work with inplace=True

You're iterating through the elements within the DataFrame, in which case I'm assuming it's type str (or being coerced to str when you replace). str.replace doesn't have an argument for inplace=....

You should be doing this instead:

dataset['ver'] = dataset['ver'].str.replace('.', '')

Python Pandas replace() not working

As far as I remember, Python Pandas was changed a little bit in replace. You should try passing over a regex keyword argument.

Like so;

report_df['Owner'].replace({'\r\nName: hwowneremail, dtype: object':''},regex=True)

pandas.DataFrame.replace is not working when I tried to replace '+' from a dataframe

You need to call the str.replace method.

df_raw['Installs'] = df_raw.Installs.str.replace('+', '')

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