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# Import pandas package
import pandas as pd
# Define a dictionary containing Students data
data = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'],
'Height': [5.1, 6.2, 5.1, 5.2],
'Qualification': ['Msc', 'MA', 'Msc', 'Msc']}
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
# Declare a list that is to be converted into a column
address = ['Delhi', 'Bangalore', 'Chennai', 'Patna']
# Using 'Address' as the column name
# and equating it to the list
df['Address'] = address
# Observe the result
print(df)
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#using the insert function:
df.insert(location, column_name, list_of_values)
#example
df.insert(0, 'new_column', ['a','b','c'])
#explanation:
#put "new_column" as first column of the dataframe
#and puts 'a','b' and 'c' as values
#using array-like access:
df['new_column_name'] = value
#df stands for dataframe
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# import pandas library
import pandas as pd
# create pandas DataFrame
df = pd.DataFrame({'team': ['India', 'South Africa', 'New Zealand', 'England'],
'points': [10, 8, 3, 5],
'runrate': [0.5, 1.4, 2, -0.6],
'wins': [5, 4, 2, 2]})
# print the DataFrame
print(df)
# declare a new list and add the values into the list
match_lost = [2, 1, 3, 4]
# assign the list to the new DataFrame Column
df["lost"] = match_lost
# Print the new DataFrame
print(df)
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#using the insert function:
df.insert(location, column_name, list_of_values)
#example
df.insert(0, 'new_column', ['a','b','c'])
#explanation:
#put "new_column" as first column of the dataframe
#and puts 'a','b' and 'c' as values
#using array access:
df['new_column_name'] = value
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# Basic syntax:
pandas_dataframe['new_column_name'] = ['list', 'of', 'column', 'values']
# Note, the list of column values must have length equal to the number
# of rows in the pandas dataframe you are adding it to.
# Add column in which all rows will be value:
pandas_dataframe['new_column_name'] = value
# Where value can be a string, an int, a float, and etc
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# import pandas library
import pandas as pd
# create pandas DataFrame
df = pd.DataFrame({'team': ['India', 'South Africa', 'New Zealand', 'England'],
'points': [10, 8, 3, 5],
'runrate': [0.5, 1.4, 2, -0.6],
'wins': [5, 4, 2, 2]})
# print the DataFrame
print(df)
# append multiple columns to Pandas DataFrame
df2 = df.assign(lost=[2, 1, 3, 4], matches_remaining=[2, 3, 1, 1])
# Print the new DataFrame
print(df2)
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group = np.random.randint(10, size=6)
df_new['Group'] = group
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# import pandas library
import pandas as pd
# create pandas DataFrame
df = pd.DataFrame({'team': ['India', 'South Africa', 'New Zealand', 'England'],
'points': [10, 8, 3, 5],
'runrate': [0.5, 1.4, 2, -0.6],
'wins': [5, 4, 2, 2]})
# print the DataFrame
print(df)
# insert the new column at the specific position
df.insert(3, "lost", [2, 1, 3, 4], True)
# Print the new DataFrame
print(df)
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In [79]:
df
Out[79]:
Date, Open, High, Low, Close
0 01-01-2015, 565, 600, 400, 450
In [80]:
df['Name'] = 'abc'
df
Out[80]:
Date, Open, High, Low, Close Name
0 01-01-2015, 565, 600, 400, 450 abc