# Rename values in Customer Fname column to uppercasedf["Customer Fname"] = df["Customer Fname"].str.upper()str.strip()函数用于删除字符串值开头或结尾可能出现的任何额外空格。# In Customer Segment column, convert names to lowercase and remove leading/trailing spacesdf['Customer Segment'] =...
# Rename values in Customer Fname column to uppercase df["Customer Fname"] = df["Customer Fname"].str.upper() str.strip()函数用于删除字符串值开头或结尾可能出现的任何额外空格。 # In Customer Segment column, convert names to lowercase and remove leading/trailing spaces df['Customer Segment'...
# Rename values in Customer Fname column to uppercase df["Customer Fname"] = df["Customer Fname"].str.upper() str.strip() 函数用于删除字符串值开头或结尾可能出现的任何额外空格。 # In Customer Segment column, convert names to lowercase and remove leading/trailing spaces df['Customer Segment...
df.rename(columns= {'Order_No_1':'OrderID','ItemNo':'ItemID'}, inplace=True)# remove special characters from column namedf.columns = df.columns.str.replace('[&,#,@,(,)]','')# remove leading/trailing space and add _ to in-betwe...
# In Customer Segment column, convert names to lowercase and remove leading/trailing spaces df['Customer Segment'] = df['Customer Segment'].str.lower().str.strip() replace()函数用于用新值替换DataFrame列中的特定值。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # Replace values in datase...
df.rename(columns= {'Order_No_1':'OrderID','ItemNo':'ItemID'}, inplace=True) # remove special characters from column name df.columns = df.columns.str.replace('[&,#,@,(,)]', '') # remove leading/trailing space and add _ to in-between spaces df.columns = df.columns.str.strip...
df.isnull().sum().sort_values(ascending=False) 1. 2. 3. 4. 5. # Check for missing values in the 'Customer Zipcode' column df['Customer Zipcode'].isnull().sum() # Check what percentage of the data frame these 3 missing values ••represent ...
You can also change the column name using thePandas lambda expression, This gives us more control and applies custom functions. The below examples add a ‘col_’ string to all column names. You can also try removing spaces from columns etc. ...
print(student_df.columns.values)# output [' name ' ' age ' 'marks ']# remove leading and trailing space from column namesstudent_df.rename(lambdax: x.strip(), axis='columns', inplace=True) print(student_df.columns.values)# Output ['name' 'age' 'marks'] ...
columns}) # Remove spaces from columns df['CreatedDate'] = pd.to_datetime(df['CreatedDate']) # Convert to datetimes df['ClosedDate'] = pd.to_datetime(df['ClosedDate']) df.index += index_start # Remove the un-interesting columns columns = ['Agency', 'CreatedDate', 'ClosedDate',...