import pandas as pd
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
'ID':[1,2,3,4,5,6],
'MATH':[80,40,70,70,60,30],
'ENGLISH':[80,70,40,50,60,30]}
df = pd.DataFrame(data=my_dict)
print(df.columns) # Object
Output
Index(['NAME', 'ID', 'MATH', 'ENGLISH'], dtype='object')
Getting any perticular column
print(df.columns[2]) # Math
By using keys()
print(df.keys())
Output
Index(['NAME', 'ID', 'MATH', 'ENGLISH'], dtype='object')
By using for loop
for cols in df.columns:
print(cols)
Output
NAME
ID
MATH
ENGLISH
To get data from each column of DataFrame
for cols in df.columns:
print(df[cols])
Calling a function by passing each column as parameter and display data.
def my_fun(cols): # function to receive column name as parameter
print(df[cols])
for cols in df.columns:
my_fun(cols)
By using list
print(list(df.columns))
Output
['NAME', 'ID', 'MATH', 'ENGLISH']
By using tolist()
print(df.columns.values.tolist())
Output
['NAME', 'ID', 'MATH', 'ENGLISH']
df = pd.DataFrame(columns=['A','B','C','D','E','F','G'])
import pandas as pd
my_dict={'NAME':['Ravi','Raju','Alex'],
'dt_start':['1-1-2020','2-1-2020','5-1-2020']
}
df = pd.DataFrame(data=my_dict)
#df['dt_start'] = pd.to_datetime(df['dt_start']) # converts to datetime data
print(df.dtypes)
l1=['Four','Three','Five','Six','Two','Three']
df['my_class']=l1
Above code will add the column at the end of the DataFrame. We can use insert()
l1=['Four','Three','Five','Six','Two','Three']
df.insert(2,'my_class',l1,True)
print(df.columns.values.tolist())
Output
['NAME', 'ID', 'my_class', 'MATH', 'ENGLISH']
Here also we have to match the existing length of data. The Boolean options at the end is to allow duplicate or not, default value is False.
l1=['Four','Three','Five','Six','Two','Three']
df2=df.assign(my_class=l1)
print(df2.columns.values.tolist())
We can use a dictionary with keys ( unique ) and value from any existing column.
d1={'Four':'Ravi','Three':'Raju','Five':'Alex','Six':'Ron',
'Two':'King','Eight':'Jack'}
df['my_class']=d1
print(df.columns.values.tolist())
We can use single value for all the rows of the new column. We will use all above methods. df['my_class']='Four' # adding column at the end
df.insert(2,'my_class','Four') # at 2nd position
df2=df.assign(my_class='Four') # new DataFrame with added column
How to add column with increasing value?
df = df.reset_index()
df = df.rename(columns={"index":"New_ID"})
df['New_ID'] = df.index + 1000 # starting from 1000
Here reset_index() adds old index as a column, and a new sequential index is used.
df.drop(labels='Page Value',axis=1,inplace=True)
df.columns = ['Page','p_view','u_view','avg']
Updating single column name
df = df.rename(columns={"my_class":"my_class4"})
if 'Gender' in df.columns :
print('Gender column is present')
else:
print('Gender column is not present')
Pandas DataFrame
describe() head() rename()
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