# convert data frame strings to numeric where possible and drop rows with non numeric content

dataframeto numbers only rows.py

# convert data frame strings to numeric where possible and drop rows with non numeric content
df23=df23.apply(pd.to_numeric, errors='ignore') # converts to numbers when possible for all  columns 
df23
c=df23.columns.to_list()
data_columns=[c[x] for x in range(1,6)]
# Eliminate invalid data from dataframe (see Example below for more context)

num_df = (df23.drop(data_columns, axis=1)
         .join(df23[data_columns].apply(pd.to_numeric, errors='coerce'))) #numeric dataframe

num_df = num_df[num_df[data_columns].notnull().all(axis=1)]
num_df
#-----------------------

# remove blanks from column names in dataframe since some tools dont accept blanks in strings
df.columns = df.columns.str.replace(' ', '_')


df.rename(columns={'two':'new_name'}, inplace=True)