I have a pandas data frame with different data types. I want to convert more than one column in the data frame to string type. I have individually done for each column but want to know if there is an efficient way?
So at present I am doing something like this:
repair['SCENARIO']=repair['SCENARIO'].astype(str)
repair['SERVICE_TYPE']= repair['SERVICE_TYPE'].astype(str)
I want a function that would help me pass multiple columns and convert them to strings.
To convert multiple columns to string, include a list of columns to your above-mentioned command:
df[['one', 'two', 'three']] = df[['one', 'two', 'three']].astype(str)
# add as many column names as you like.
That means that one way to convert all columns is to construct the list of columns like this:
all_columns = list(df) # Creates list of all column headers
df[all_columns] = df[all_columns].astype(str)
Note that the latter can also be done directly (see comments).
df = df.astype(str) ?
df = df.astype(str) shape (50000, 23000) and it crashed (in interactive mode). Thank you
I know this is an old question, but I was looking for a way to turn all columns with an object dtype to strings as a workaround for a bug I discovered in rpy2. I'm working with large dataframes, so didn't want to list each column explicitly. This seemed to work well for me so I thought I'd share in case it helps someone else.
stringcols = df.select_dtypes(include='object').columns
df[stringcols] = df[stringcols].fillna('').astype(str)
The "fillna('')" prevents NaN entries from getting converted to the string 'nan' by replacing with an empty string instead.
You can also use list comprehension:
df = [df[col_name].astype(str) for col_name in df.columns]
You can also insert a condition to test if the columns should be converted - for example:
df = [df[col_name].astype(str) for col_name in df.columns if 'to_str' in col_name]
