There are several ways to create DataFrame in pandas and add data to it as columns and rows.
import pandas as pd
#first method - create empty dataframe
df = pd.DataFrame()
# append columns and rows to this dataframe
df['username'] = ['john', 'neil', 'curtis']
df['status'] = ['active', 'disabled', 'active']
print(df)
#----------------------------------------------------------------------------------#
#second method - create dataframe with columns
df = pd.DataFrame(columns = ['username', 'status'])
# append rows to the abo
df = df.append({'username' : 'john', 'status' : 'active'}, ignore_index = True)
df = df.append({'username' : 'neil', 'status' : 'disabled'}, ignore_index = True)
df = df.append({'username' : 'curtis', 'status' : 'active'}, ignore_index = True)
print(df)
#----------------------------------------------------------------------------------#
#make data frame with rows having - NaN values at index - a, b and c
df = pd.DataFrame(columns = ['username', 'status'], index = ['a', 'b', 'c'])
print(df)
# +---+----------+--------+
# | | username | status |
# +---+----------+--------+
# | a | NaN | NaN |
# | b | NaN | NaN |
# | c | NaN | NaN |
# +---+----------+--------+
# add rows at already created indexes
df.loc['a'] = ['john', 'active']
df.loc['b'] = ['neil', 'disabled']
df.loc['c'] = ['curtis', 'active']
print(df)
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