Description stringlengths 9 105 | Link stringlengths 45 135 | Code stringlengths 10 26.8k | Test_Case stringlengths 9 202 | Merge stringlengths 63 27k |
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Create a Pandas DataFrame from List of Dicts | https://www.geeksforgeeks.org/create-a-pandas-dataframe-from-list-of-dicts/ | import pandas as pd
# Initialise data to lists.
data = [
{"Geeks": "dataframe", "For": "using", "geeks": "list"},
{"Geeks": 10, "For": 20, "geeks": 30},
]
# With two column indices, values same
# as dictionary keys
df1 = pd.DataFrame(data, index=["ind1", "ind2"], columns=["Geeks", "For"])
# With two column i... |
#Output : Geeks For geeks | Create a Pandas DataFrame from List of Dicts
import pandas as pd
# Initialise data to lists.
data = [
{"Geeks": "dataframe", "For": "using", "geeks": "list"},
{"Geeks": 10, "For": 20, "geeks": 30},
]
# With two column indices, values same
# as dictionary keys
df1 = pd.DataFrame(data, index=["ind1", "ind2"], c... |
Python | Convert list of nested dictionary into Pandas dataframe | https://www.geeksforgeeks.org/python-convert-list-of-nested-dictionary-into-pandas-dataframe/ | # importing pandas
import pandas as pd
# List of nested dictionary initialization
list = [
{
"Student": [
{"Exam": 90, "Grade": "a"},
{"Exam": 99, "Grade": "b"},
{"Exam": 97, "Grade": "c"},
],
"Name": "Paras Jain",
},
{
"Student": [{"Exam"... |
#Output : Name Maths Physics Chemistry
| Python | Convert list of nested dictionary into Pandas dataframe
# importing pandas
import pandas as pd
# List of nested dictionary initialization
list = [
{
"Student": [
{"Exam": 90, "Grade": "a"},
{"Exam": 99, "Grade": "b"},
{"Exam": 97, "Grade": "c"},
],
... |
Python | Convert list of nested dictionary into Pandas dataframe | https://www.geeksforgeeks.org/python-convert-list-of-nested-dictionary-into-pandas-dataframe/ | # rows list initialization
rows = []
# appending rows
for data in list:
data_row = data["Student"]
time = data["Name"]
for row in data_row:
row["Name"] = time
rows.append(row)
# using data frame
df = pd.DataFrame(rows)
# print(df) |
#Output : Name Maths Physics Chemistry
| Python | Convert list of nested dictionary into Pandas dataframe
# rows list initialization
rows = []
# appending rows
for data in list:
data_row = data["Student"]
time = data["Name"]
for row in data_row:
row["Name"] = time
rows.append(row)
# using data frame
df = pd.DataFrame(rows)
# pr... |
Python | Convert list of nested dictionary into Pandas dataframe | https://www.geeksforgeeks.org/python-convert-list-of-nested-dictionary-into-pandas-dataframe/ | # using pivot_table
df = df.pivot_table(index="Name", columns=["Grade"], values=["Exam"]).reset_index()
# Defining columns
df.columns = ["Name", "Maths", "Physics", "Chemistry"]
# print dataframe
print(df) |
#Output : Name Maths Physics Chemistry
| Python | Convert list of nested dictionary into Pandas dataframe
# using pivot_table
df = df.pivot_table(index="Name", columns=["Grade"], values=["Exam"]).reset_index()
# Defining columns
df.columns = ["Name", "Maths", "Physics", "Chemistry"]
# print dataframe
print(df)
#Output : Name Maths Physics ... |
Python | Convert list of nested dictionary into Pandas dataframe | https://www.geeksforgeeks.org/python-convert-list-of-nested-dictionary-into-pandas-dataframe/ | # Python program to convert list of nested
# dictionary into Pandas dataframe
# importing pandas
import pandas as pd
# List of list of dictionary initialization
list = [
{
"Student": [
{"Exam": 90, "Grade": "a"},
{"Exam": 99, "Grade": "b"},
{"Exam": 97, "Grade": "c"},
... |
#Output : Name Maths Physics Chemistry
| Python | Convert list of nested dictionary into Pandas dataframe
# Python program to convert list of nested
# dictionary into Pandas dataframe
# importing pandas
import pandas as pd
# List of list of dictionary initialization
list = [
{
"Student": [
{"Exam": 90, "Grade": "a"},
{"Ex... |
Creating a dataframe from Pandas series | https://www.geeksforgeeks.org/creating-a-dataframe-from-pandas-series/ | # importing pandas library
import pandas as pd
# Creating a list
author = ["Jitender", "Purnima", "Arpit", "Jyoti"]
# Creating a Series by passing list
# variable to Series() function
auth_series = pd.Series(author)
# Printing Series
print(auth_series) |
#Output : 0 Jitender | Creating a dataframe from Pandas series
# importing pandas library
import pandas as pd
# Creating a list
author = ["Jitender", "Purnima", "Arpit", "Jyoti"]
# Creating a Series by passing list
# variable to Series() function
auth_series = pd.Series(author)
# Printing Series
print(auth_series)
#Output : 0 Jitender... |
Creating a dataframe from Pandas series | https://www.geeksforgeeks.org/creating-a-dataframe-from-pandas-series/ | print(type(auth_series)) |
#Output : 0 Jitender | Creating a dataframe from Pandas series
print(type(auth_series))
#Output : 0 Jitender
[END] |
Creating a dataframe from Pandas series | https://www.geeksforgeeks.org/creating-a-dataframe-from-pandas-series/ | # Importing Pandas library
import pandas as pd
# Creating two lists
author = ["Jitender", "Purnima", "Arpit", "Jyoti"]
article = [210, 211, 114, 178]
# Creating two Series by passing lists
auth_series = pd.Series(author)
article_series = pd.Series(article)
# Creating a dictionary by passing Series objects as values
... |
#Output : 0 Jitender | Creating a dataframe from Pandas series
# Importing Pandas library
import pandas as pd
# Creating two lists
author = ["Jitender", "Purnima", "Arpit", "Jyoti"]
article = [210, 211, 114, 178]
# Creating two Series by passing lists
auth_series = pd.Series(author)
article_series = pd.Series(article)
# Creating a diction... |
Creating a dataframe from Pandas series | https://www.geeksforgeeks.org/creating-a-dataframe-from-pandas-series/ | # Importing pandas library
import pandas as pd
# Creating Series
auth_series = pd.Series(["Jitender", "Purnima", "Arpit", "Jyoti"])
article_series = pd.Series([210, 211, 114, 178])
# Creating Dictionary
frame = {"Author": auth_series, "Article": article_series}
# Creating Dataframe
result = pd.DataFrame(frame)
# Creat... |
#Output : 0 Jitender | Creating a dataframe from Pandas series
# Importing pandas library
import pandas as pd
# Creating Series
auth_series = pd.Series(["Jitender", "Purnima", "Arpit", "Jyoti"])
article_series = pd.Series([210, 211, 114, 178])
# Creating Dictionary
frame = {"Author": auth_series, "Article": article_series}
# Creating Datafr... |
Creating a dataframe from Pandas series | https://www.geeksforgeeks.org/creating-a-dataframe-from-pandas-series/ | # Importing pandas library
import pandas as pd
# Creating Series
auth_series = pd.Series(["Jitender", "Purnima", "Arpit", "Jyoti"])
article_series = pd.Series([210, 211, 114, 178])
# Creating Dictionary
frame = {"Author": auth_series, "Article": article_series}
# Creating Dataframe
result = pd.DataFrame(frame)
# Creat... |
#Output : 0 Jitender | Creating a dataframe from Pandas series
# Importing pandas library
import pandas as pd
# Creating Series
auth_series = pd.Series(["Jitender", "Purnima", "Arpit", "Jyoti"])
article_series = pd.Series([210, 211, 114, 178])
# Creating Dictionary
frame = {"Author": auth_series, "Article": article_series}
# Creating Datafr... |
Creating a dataframe from Pandas series | https://www.geeksforgeeks.org/creating-a-dataframe-from-pandas-series/ | # Importing pandas library
import pandas as pd
# Creating dictionary of Series
dict1 = {
"Auth_Name": pd.Series(["Jitender", "Purnima", "Arpit", "Jyoti"]),
"Author_Book_No": pd.Series([210, 211, 114, 178]),
"Age": pd.Series([21, 21, 24, 23]),
}
# Creating Dataframe
df = pd.DataFrame(dict1)
# Printing data... |
#Output : 0 Jitender | Creating a dataframe from Pandas series
# Importing pandas library
import pandas as pd
# Creating dictionary of Series
dict1 = {
"Auth_Name": pd.Series(["Jitender", "Purnima", "Arpit", "Jyoti"]),
"Author_Book_No": pd.Series([210, 211, 114, 178]),
"Age": pd.Series([21, 21, 24, 23]),
}
# Creating Dataframe
... |
Creating a dataframe from Pandas series | https://www.geeksforgeeks.org/creating-a-dataframe-from-pandas-series/ | # Importing pandas library
import pandas as pd
# Creating dictionary of Series
dict1 = {
"Auth_Name": pd.Series(["Jitender", "Purnima", "Arpit", "Jyoti"]),
"Author_Book_No": pd.Series([210, 211, 114, 178]),
"Age": pd.Series([21, 21, 24, 23]),
}
# Creating Dataframe
df = pd.DataFrame(dict1, index=["SNo1", ... |
#Output : 0 Jitender | Creating a dataframe from Pandas series
# Importing pandas library
import pandas as pd
# Creating dictionary of Series
dict1 = {
"Auth_Name": pd.Series(["Jitender", "Purnima", "Arpit", "Jyoti"]),
"Author_Book_No": pd.Series([210, 211, 114, 178]),
"Age": pd.Series([21, 21, 24, 23]),
}
# Creating Dataframe
... |
Creating a dataframe from Pandas series | https://www.geeksforgeeks.org/creating-a-dataframe-from-pandas-series/ | # This code is provided by Sheetal Verma
# Importing pandas library
import pandas as pd
# Creating dictionary of Series
dict1 = {
"Auth_Name": pd.Series(
["Jitender", "Purnima", "Arpit", "Jyoti"],
index=["SNo1", "SNo2", "SNo3", "SNo4"],
),
"Author_Book_No": pd.Series(
[210, 211, 114... |
#Output : 0 Jitender | Creating a dataframe from Pandas series
# This code is provided by Sheetal Verma
# Importing pandas library
import pandas as pd
# Creating dictionary of Series
dict1 = {
"Auth_Name": pd.Series(
["Jitender", "Purnima", "Arpit", "Jyoti"],
index=["SNo1", "SNo2", "SNo3", "SNo4"],
),
"Author_Boo... |
Mapping external values to dataframe values in Pandas | https://www.geeksforgeeks.org/mapping-external-values-to-dataframe-values-in-pandas/ | # Creating new dataframe
import pandas as pd
initial_data = {
"First_name": ["Ram", "Mohan", "Tina", "Jeetu", "Meera"],
"Last_name": ["Kumar", "Sharma", "Ali", "Gandhi", "Kumari"],
"Age": [42, 52, 36, 21, 23],
"City": ["Mumbai", "Noida", "Pune", "Delhi", "Bihar"],
}
df = pd.DataFrame(initial_data, col... |
#Output :
| Mapping external values to dataframe values in Pandas
# Creating new dataframe
import pandas as pd
initial_data = {
"First_name": ["Ram", "Mohan", "Tina", "Jeetu", "Meera"],
"Last_name": ["Kumar", "Sharma", "Ali", "Gandhi", "Kumari"],
"Age": [42, 52, 36, 21, 23],
"City": ["Mumbai", "Noida", "Pune", "De... |
Mapping external values to dataframe values in Pandas | https://www.geeksforgeeks.org/mapping-external-values-to-dataframe-values-in-pandas/ | # Creating new dataframe
import pandas as pd
initial_data = {
"First_name": ["Ram", "Mohan", "Tina", "Jeetu", "Meera"],
"Last_name": ["Kumar", "Sharma", "Ali", "Gandhi", "Kumari"],
"Age": [42, 52, 36, 21, 23],
"City": ["Mumbai", "Noida", "Pune", "Delhi", "Bihar"],
}
df = pd.DataFrame(initial_data, col... |
#Output :
| Mapping external values to dataframe values in Pandas
# Creating new dataframe
import pandas as pd
initial_data = {
"First_name": ["Ram", "Mohan", "Tina", "Jeetu", "Meera"],
"Last_name": ["Kumar", "Sharma", "Ali", "Gandhi", "Kumari"],
"Age": [42, 52, 36, 21, 23],
"City": ["Mumbai", "Noida", "Pune", "De... |
Mapping external values to dataframe values in Pandas | https://www.geeksforgeeks.org/mapping-external-values-to-dataframe-values-in-pandas/ | # Creating new dataframe
import pandas as pd
initial_data = {
"First_name": ["Ram", "Mohan", "Tina", "Jeetu", "Meera"],
"Last_name": ["Kumar", "Sharma", "Ali", "Gandhi", "Kumari"],
"Age": [42, 52, 36, 21, 23],
"City": ["Mumbai", "Noida", "Pune", "Delhi", "Bihar"],
}
df = pd.DataFrame(initial_data, col... |
#Output :
| Mapping external values to dataframe values in Pandas
# Creating new dataframe
import pandas as pd
initial_data = {
"First_name": ["Ram", "Mohan", "Tina", "Jeetu", "Meera"],
"Last_name": ["Kumar", "Sharma", "Ali", "Gandhi", "Kumari"],
"Age": [42, 52, 36, 21, 23],
"City": ["Mumbai", "Noida", "Pune", "De... |
How to iterate over rows in Pandas Dataframe | https://www.geeksforgeeks.org/how-to-iterate-over-rows-in-pandas-dataframe/ | # importing pandas
import pandas as pd
# list of dicts
input_df = [
{"name": "Sujeet", "age": 10},
{"name": "Sameer", "age": 11},
{"name": "Sumit", "age": 12},
]
df = pd.DataFrame(input_df)
print("Original DataFrame: \n", df)
print("\nRows iterated using iterrows() : ")
for index, row in df.iterrows():
... |
#Output : Original DataFrame: | How to iterate over rows in Pandas Dataframe
# importing pandas
import pandas as pd
# list of dicts
input_df = [
{"name": "Sujeet", "age": 10},
{"name": "Sameer", "age": 11},
{"name": "Sumit", "age": 12},
]
df = pd.DataFrame(input_df)
print("Original DataFrame: \n", df)
print("\nRows iterated using iter... |
How to iterate over rows in Pandas Dataframe | https://www.geeksforgeeks.org/how-to-iterate-over-rows-in-pandas-dataframe/ | # importing pandas
import pandas as pd
# list of dicts
input_df = [
{"name": "Sujeet", "age": 10},
{"name": "Sameer", "age": 110},
{"name": "Sumit", "age": 120},
]
df = pd.DataFrame(input_df)
print("Original DataFrame: \n", df)
print("\nRows iterated using itertuples() : ")
for row in df.itertuples():
... |
#Output : Original DataFrame: | How to iterate over rows in Pandas Dataframe
# importing pandas
import pandas as pd
# list of dicts
input_df = [
{"name": "Sujeet", "age": 10},
{"name": "Sameer", "age": 110},
{"name": "Sumit", "age": 120},
]
df = pd.DataFrame(input_df)
print("Original DataFrame: \n", df)
print("\nRows iterated using ite... |
Different ways to iterate over rows in Pandas Dataframe | https://www.geeksforgeeks.org/different-ways-to-iterate-over-rows-in-pandas-dataframe/ | # import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {
"Name": ["Ankit", "Amit", "Aishwarya", "Priyanka"],
"Age": [21, 19, 20, 18],
"Stream": ["Math", "Commerce", "Arts", "Biology"],
"Percentage": [88, 92, 95, 70],
}
# Convert the dictionary into Data... |
#Output : Given Dataframe :
| Different ways to iterate over rows in Pandas Dataframe
# import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {
"Name": ["Ankit", "Amit", "Aishwarya", "Priyanka"],
"Age": [21, 19, 20, 18],
"Stream": ["Math", "Commerce", "Arts", "Biology"],
"Percentage":... |
Different ways to iterate over rows in Pandas Dataframe | https://www.geeksforgeeks.org/different-ways-to-iterate-over-rows-in-pandas-dataframe/ | # import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {
"Name": ["Ankit", "Amit", "Aishwarya", "Priyanka"],
"Age": [21, 19, 20, 18],
"Stream": ["Math", "Commerce", "Arts", "Biology"],
"Percentage": [88, 92, 95, 70],
}
# Convert the dictionary into Data... |
#Output : Given Dataframe :
| Different ways to iterate over rows in Pandas Dataframe
# import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {
"Name": ["Ankit", "Amit", "Aishwarya", "Priyanka"],
"Age": [21, 19, 20, 18],
"Stream": ["Math", "Commerce", "Arts", "Biology"],
"Percentage":... |
Different ways to iterate over rows in Pandas Dataframe | https://www.geeksforgeeks.org/different-ways-to-iterate-over-rows-in-pandas-dataframe/ | # import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {
"Name": ["Ankit", "Amit", "Aishwarya", "Priyanka"],
"Age": [21, 19, 20, 18],
"Stream": ["Math", "Commerce", "Arts", "Biology"],
"Percentage": [88, 92, 95, 70],
}
# Convert the dictionary into Data... |
#Output : Given Dataframe :
| Different ways to iterate over rows in Pandas Dataframe
# import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {
"Name": ["Ankit", "Amit", "Aishwarya", "Priyanka"],
"Age": [21, 19, 20, 18],
"Stream": ["Math", "Commerce", "Arts", "Biology"],
"Percentage":... |
Different ways to iterate over rows in Pandas Dataframe | https://www.geeksforgeeks.org/different-ways-to-iterate-over-rows-in-pandas-dataframe/ | # import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {
"Name": ["Ankit", "Amit", "Aishwarya", "Priyanka"],
"Age": [21, 19, 20, 18],
"Stream": ["Math", "Commerce", "Arts", "Biology"],
"Percentage": [88, 92, 95, 70],
}
# Convert the dictionary into Data... |
#Output : Given Dataframe :
| Different ways to iterate over rows in Pandas Dataframe
# import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {
"Name": ["Ankit", "Amit", "Aishwarya", "Priyanka"],
"Age": [21, 19, 20, 18],
"Stream": ["Math", "Commerce", "Arts", "Biology"],
"Percentage":... |
Different ways to iterate over rows in Pandas Dataframe | https://www.geeksforgeeks.org/different-ways-to-iterate-over-rows-in-pandas-dataframe/ | # import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {
"Name": ["Ankit", "Amit", "Aishwarya", "Priyanka"],
"Age": [21, 19, 20, 18],
"Stream": ["Math", "Commerce", "Arts", "Biology"],
"Percentage": [88, 92, 95, 70],
}
# Convert the dictionary into Data... |
#Output : Given Dataframe :
| Different ways to iterate over rows in Pandas Dataframe
# import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {
"Name": ["Ankit", "Amit", "Aishwarya", "Priyanka"],
"Age": [21, 19, 20, 18],
"Stream": ["Math", "Commerce", "Arts", "Biology"],
"Percentage":... |
Different ways to iterate over rows in Pandas Dataframe | https://www.geeksforgeeks.org/different-ways-to-iterate-over-rows-in-pandas-dataframe/ | # import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {
"Name": ["Ankit", "Amit", "Aishwarya", "Priyanka"],
"Age": [21, 19, 20, 18],
"Stream": ["Math", "Commerce", "Arts", "Biology"],
"Percentage": [88, 92, 95, 70],
}
# Convert the dictionary into Data... |
#Output : Given Dataframe :
| Different ways to iterate over rows in Pandas Dataframe
# import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {
"Name": ["Ankit", "Amit", "Aishwarya", "Priyanka"],
"Age": [21, 19, 20, 18],
"Stream": ["Math", "Commerce", "Arts", "Biology"],
"Percentage":... |
Selementsect any row from a Dataframe using iloc[] and iat[] in Pandas | https://www.geeksforgeeks.org/select-any-row-from-a-dataframe-using-iloc-and-iat-in-pandas/ | import pandas as pd
# Create the dataframe
df = pd.DataFrame(
{
"Date": ["10/2/2011", "11/2/2011", "12/2/2011", "13/2/11"],
"Event": ["Music", "Poetry", "Theatre", "Comedy"],
"Cost": [10000, 5000, 15000, 2000],
}
)
# Create an empty list
Row_list = []
# Iterate over each row
for i in ... |
#Output : [[10000, '10/2/2011', 'Music'], [5000, '11/2/2011', 'Poetry'], | Selementsect any row from a Dataframe using iloc[] and iat[] in Pandas
import pandas as pd
# Create the dataframe
df = pd.DataFrame(
{
"Date": ["10/2/2011", "11/2/2011", "12/2/2011", "13/2/11"],
"Event": ["Music", "Poetry", "Theatre", "Comedy"],
"Cost": [10000, 5000, 15000, 2000],
}
)
... |
Selementsect any row from a Dataframe using iloc[] and iat[] in Pandas | https://www.geeksforgeeks.org/select-any-row-from-a-dataframe-using-iloc-and-iat-in-pandas/ | # importing pandas as pd
import pandas as pd
# Create the dataframe
df = pd.DataFrame(
{
"Date": ["10/2/2011", "11/2/2011", "12/2/2011", "13/2/11"],
"Event": ["Music", "Poetry", "Theatre", "Comedy"],
"Cost": [10000, 5000, 15000, 2000],
}
)
# Create an empty list
Row_list = []
# Iterat... |
#Output : [[10000, '10/2/2011', 'Music'], [5000, '11/2/2011', 'Poetry'], | Selementsect any row from a Dataframe using iloc[] and iat[] in Pandas
# importing pandas as pd
import pandas as pd
# Create the dataframe
df = pd.DataFrame(
{
"Date": ["10/2/2011", "11/2/2011", "12/2/2011", "13/2/11"],
"Event": ["Music", "Poetry", "Theatre", "Comedy"],
"Cost": [10000, 5000... |
Limited rows selementsection with given column in Pandas | Python | https://www.geeksforgeeks.org/limited-rows-selection-with-given-column-in-pandas-python/ | # Import pandas package
import pandas as pd
# Define a dictionary containing employee data
data = {
"Name": ["Jai", "Princi", "Gaurav", "Anuj"],
"Age": [27, 24, 22, 32],
"Address": ["Delhi", "Kanpur", "Allahabad", "Kannauj"],
"Qualification": ["Msc", "MA", "MCA", "Phd"],
}
# Convert the dictionary int... |
#Output : Name Qualification
| Limited rows selementsection with given column in Pandas | Python
# Import pandas package
import pandas as pd
# Define a dictionary containing employee data
data = {
"Name": ["Jai", "Princi", "Gaurav", "Anuj"],
"Age": [27, 24, 22, 32],
"Address": ["Delhi", "Kanpur", "Allahabad", "Kannauj"],
"Qualificat... |
Limited rows selementsection with given column in Pandas | Python | https://www.geeksforgeeks.org/limited-rows-selection-with-given-column-in-pandas-python/ | # Import pandas package
import pandas as pd
# Define a dictionary containing employee data
data = {
"Name": ["Jai", "Princi", "Gaurav", "Anuj"],
"Age": [27, 24, 22, 32],
"Address": ["Delhi", "Kanpur", "Allahabad", "Kannauj"],
"Qualification": ["Msc", "MA", "MCA", "Phd"],
}
# Convert the dictionary int... |
#Output : Name Qualification
| Limited rows selementsection with given column in Pandas | Python
# Import pandas package
import pandas as pd
# Define a dictionary containing employee data
data = {
"Name": ["Jai", "Princi", "Gaurav", "Anuj"],
"Age": [27, 24, 22, 32],
"Address": ["Delhi", "Kanpur", "Allahabad", "Kannauj"],
"Qualificat... |
Limited rows selementsection with given column in Pandas | Python | https://www.geeksforgeeks.org/limited-rows-selection-with-given-column-in-pandas-python/ | # Import pandas package
import pandas as pd
# Define a dictionary containing employee data
data = {
"Name": ["Jai", "Princi", "Gaurav", "Anuj"],
"Age": [27, 24, 22, 32],
"Address": ["Delhi", "Kanpur", "Allahabad", "Kannauj"],
"Qualification": ["Msc", "MA", "MCA", "Phd"],
}
# Convert the dictionary int... |
#Output : Name Qualification
| Limited rows selementsection with given column in Pandas | Python
# Import pandas package
import pandas as pd
# Define a dictionary containing employee data
data = {
"Name": ["Jai", "Princi", "Gaurav", "Anuj"],
"Age": [27, 24, 22, 32],
"Address": ["Delhi", "Kanpur", "Allahabad", "Kannauj"],
"Qualificat... |
Sorting rows in pandas DataFrame | https://www.geeksforgeeks.org/sorting-rows-in-pandas-dataframe/ | # import modules
import pandas as pd
# create dataframe
data = {
"name": ["Simon", "Marsh", "Gaurav", "Alex", "Selena"],
"Maths": [8, 5, 6, 9, 7],
"Science": [7, 9, 5, 4, 7],
"English": [7, 4, 7, 6, 8],
}
df = pd.DataFrame(data)
# Sort the dataframe?????????s rows by Science,
# in descending order
a ... |
#Output :
| Sorting rows in pandas DataFrame
# import modules
import pandas as pd
# create dataframe
data = {
"name": ["Simon", "Marsh", "Gaurav", "Alex", "Selena"],
"Maths": [8, 5, 6, 9, 7],
"Science": [7, 9, 5, 4, 7],
"English": [7, 4, 7, 6, 8],
}
df = pd.DataFrame(data)
# Sort the dataframe?????????s rows by ... |
Sorting rows in pandas DataFrame | https://www.geeksforgeeks.org/sorting-rows-in-pandas-dataframe/ | # import modules
import pandas as pd
# create dataframe
data = {
"name": ["Simon", "Marsh", "Gaurav", "Alex", "Selena"],
"Maths": [8, 5, 6, 9, 7],
"Science": [7, 9, 5, 4, 7],
"English": [7, 4, 7, 6, 8],
}
df = pd.DataFrame(data)
# Sort the dataframe?????????s rows by Maths
# and then by English, in a... |
#Output :
| Sorting rows in pandas DataFrame
# import modules
import pandas as pd
# create dataframe
data = {
"name": ["Simon", "Marsh", "Gaurav", "Alex", "Selena"],
"Maths": [8, 5, 6, 9, 7],
"Science": [7, 9, 5, 4, 7],
"English": [7, 4, 7, 6, 8],
}
df = pd.DataFrame(data)
# Sort the dataframe?????????s rows by ... |
Sorting rows in pandas DataFrame | https://www.geeksforgeeks.org/sorting-rows-in-pandas-dataframe/ | import pandas as pd
# create dataframe
data = {
"name": ["Simon", "Marsh", "Gaurav", "Alex", "Selena"],
"Maths": [8, 5, 6, 9, 7],
"Science": [7, 9, 5, 4, 7],
"English": [7, 4, 7, 6, 8],
}
df = pd.DataFrame(data)
a = df.sort_values(by="Science", na_position="first")
print(a) |
#Output :
| Sorting rows in pandas DataFrame
import pandas as pd
# create dataframe
data = {
"name": ["Simon", "Marsh", "Gaurav", "Alex", "Selena"],
"Maths": [8, 5, 6, 9, 7],
"Science": [7, 9, 5, 4, 7],
"English": [7, 4, 7, 6, 8],
}
df = pd.DataFrame(data)
a = df.sort_values(by="Science", na_position="first")
pr... |
Selementsect row with maximum and minimum value in Pandas dataframe | https://www.geeksforgeeks.org/select-row-with-maximum-and-minimum-value-in-pandas-dataframe/ | # importing pandas and numpy
import pandas as pd
import numpy as np
# data of 2018 drivers world championship
dict1 = {
"Driver": [
"Hamilton",
"Vettel",
"Raikkonen",
"Verstappen",
"Bottas",
"Ricciardo",
"Hulkenberg",
"Perez",
"Magnussen",
... |
#Output : 39 | Selementsect row with maximum and minimum value in Pandas dataframe
# importing pandas and numpy
import pandas as pd
import numpy as np
# data of 2018 drivers world championship
dict1 = {
"Driver": [
"Hamilton",
"Vettel",
"Raikkonen",
"Verstappen",
"Bottas",
"Ricciar... |
Selementsect row with maximum and minimum value in Pandas dataframe | https://www.geeksforgeeks.org/select-row-with-maximum-and-minimum-value-in-pandas-dataframe/ | # creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# the result shows max on
# Driver, Points, Age columns.
print(df.max()) |
#Output : 39 | Selementsect row with maximum and minimum value in Pandas dataframe
# creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# the result shows max on
# Driver, Points, Age columns.
print(df.max())
#Output : 39
[END] |
Selementsect row with maximum and minimum value in Pandas dataframe | https://www.geeksforgeeks.org/select-row-with-maximum-and-minimum-value-in-pandas-dataframe/ | # creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# Who scored more points ?
print(df[df.Points == df.Points.max()]) |
#Output : 39 | Selementsect row with maximum and minimum value in Pandas dataframe
# creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# Who scored more points ?
print(df[df.Points == df.Points.max()])
#Output : 39
[END] |
Selementsect row with maximum and minimum value in Pandas dataframe | https://www.geeksforgeeks.org/select-row-with-maximum-and-minimum-value-in-pandas-dataframe/ | # creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# what is the maximum age ?
print(df.Age.max()) |
#Output : 39 | Selementsect row with maximum and minimum value in Pandas dataframe
# creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# what is the maximum age ?
print(df.Age.max())
#Output : 39
[END] |
Selementsect row with maximum and minimum value in Pandas dataframe | https://www.geeksforgeeks.org/select-row-with-maximum-and-minimum-value-in-pandas-dataframe/ | # creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# Which row has maximum age |
# who is the oldest driver ?
print(df[df.Age == df.Age.max()]) |
#Output : 39 | Selementsect row with maximum and minimum value in Pandas dataframe
# creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# Which row has maximum age |
# who is the oldest driver ?
print(df[df.Age == df.Age.max()])
#Output : 39
[END] |
Selementsect row with maximum and minimum value in Pandas dataframe | https://www.geeksforgeeks.org/select-row-with-maximum-and-minimum-value-in-pandas-dataframe/ | # creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# the result shows min on
# Driver, Points, Age columns.
print(df.min()) |
#Output : 39 | Selementsect row with maximum and minimum value in Pandas dataframe
# creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# the result shows min on
# Driver, Points, Age columns.
print(df.min())
#Output : 39
[END] |
Selementsect row with maximum and minimum value in Pandas dataframe | https://www.geeksforgeeks.org/select-row-with-maximum-and-minimum-value-in-pandas-dataframe/ | # creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# Who scored less points ?
print(df[df.Points == df.Points.min()]) |
#Output : 39 | Selementsect row with maximum and minimum value in Pandas dataframe
# creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# Who scored less points ?
print(df[df.Points == df.Points.min()])
#Output : 39
[END] |
Selementsect row with maximum and minimum value in Pandas dataframe | https://www.geeksforgeeks.org/select-row-with-maximum-and-minimum-value-in-pandas-dataframe/ | # creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# Which row has maximum age |
# who is the youngest driver ?
print(df[df.Age == df.Age.min()]) |
#Output : 39 | Selementsect row with maximum and minimum value in Pandas dataframe
# creating dataframe using DataFrame constructor
df = pd.DataFrame(dict1)
# Which row has maximum age |
# who is the youngest driver ?
print(df[df.Age == df.Age.min()])
#Output : 39
[END] |
Create a pandas column using for loop | https://www.geeksforgeeks.org/create-a-pandas-column-using-for-loop/ | # importing libraries
import pandas as pd
import numpy as np
raw_Data = {
"Voter_name": [
"Geek1",
"Geek2",
"Geek3",
"Geek4",
"Geek5",
"Geek6",
"Geek7",
"Geek8",
],
"Voter_age": [15, 23, 25, 9, 67, 54, 42, np.NaN],
}
df = pd.DataFrame(raw_Dat... |
#Output :
| Create a pandas column using for loop
# importing libraries
import pandas as pd
import numpy as np
raw_Data = {
"Voter_name": [
"Geek1",
"Geek2",
"Geek3",
"Geek4",
"Geek5",
"Geek6",
"Geek7",
"Geek8",
],
"Voter_age": [15, 23, 25, 9, 67, 54, 42,... |
How to rename columns in Pandas DataFrame | https://www.geeksforgeeks.org/how-to-rename-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# Define a dictionary containing ICC rankings
rankings = {
"test": ["India", "South Africa", "England", "New Zealand", "Australia"],
"odi": ["England", "India", "New Zealand", "South Africa", "Pakistan"],
"t20": ["Pakistan", "India", "Australia", "England", "New ... |
#Output : col_test_1 col_odi_1 col_t20_1
| How to rename columns in Pandas DataFrame
# Import pandas package
import pandas as pd
# Define a dictionary containing ICC rankings
rankings = {
"test": ["India", "South Africa", "England", "New Zealand", "Australia"],
"odi": ["England", "India", "New Zealand", "South Africa", "Pakistan"],
"t20": ["Pakista... |
How to rename columns in Pandas DataFrame | https://www.geeksforgeeks.org/how-to-rename-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# Define a dictionary containing ICC rankings
rankings = {
"test": ["India", "South Africa", "England", "New Zealand", "Australia"],
"odi": ["England", "India", "New Zealand", "South Africa", "Pakistan"],
"t20": ["Pakistan", "India", "Australia", "England", "New ... |
#Output : col_test_1 col_odi_1 col_t20_1
| How to rename columns in Pandas DataFrame
# Import pandas package
import pandas as pd
# Define a dictionary containing ICC rankings
rankings = {
"test": ["India", "South Africa", "England", "New Zealand", "Australia"],
"odi": ["England", "India", "New Zealand", "South Africa", "Pakistan"],
"t20": ["Pakista... |
How to rename columns in Pandas DataFrame | https://www.geeksforgeeks.org/how-to-rename-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# Define a dictionary containing ICC rankings
rankings = {
"test": ["India", "South Africa", "England", "New Zealand", "Australia"],
"odi": ["England", "India", "New Zealand", "South Africa", "Pakistan"],
"t20": ["Pakistan", "India", "Australia", "England", "New ... |
#Output : col_test_1 col_odi_1 col_t20_1
| How to rename columns in Pandas DataFrame
# Import pandas package
import pandas as pd
# Define a dictionary containing ICC rankings
rankings = {
"test": ["India", "South Africa", "England", "New Zealand", "Australia"],
"odi": ["England", "India", "New Zealand", "South Africa", "Pakistan"],
"t20": ["Pakista... |
How to rename columns in Pandas DataFrame | https://www.geeksforgeeks.org/how-to-rename-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# Define a dictionary containing ICC rankings
rankings = {
"test": ["India", "South Africa", "England", "New Zealand", "Australia"],
"odi": ["England", "India", "New Zealand", "South Africa", "Pakistan"],
"t20": ["Pakistan", "India", "Australia", "England", "New ... |
#Output : col_test_1 col_odi_1 col_t20_1
| How to rename columns in Pandas DataFrame
# Import pandas package
import pandas as pd
# Define a dictionary containing ICC rankings
rankings = {
"test": ["India", "South Africa", "England", "New Zealand", "Australia"],
"odi": ["England", "India", "New Zealand", "South Africa", "Pakistan"],
"t20": ["Pakista... |
How to rename columns in Pandas DataFrame | https://www.geeksforgeeks.org/how-to-rename-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# Define a dictionary containing ICC rankings
rankings = {
"test": ["India", "South Africa", "England", "New Zealand", "Australia"],
"odi": ["England", "India", "New Zealand", "South Africa", "Pakistan"],
"t20": ["Pakistan", "India", "Australia", "England", "New ... |
#Output : col_test_1 col_odi_1 col_t20_1
| How to rename columns in Pandas DataFrame
# Import pandas package
import pandas as pd
# Define a dictionary containing ICC rankings
rankings = {
"test": ["India", "South Africa", "England", "New Zealand", "Australia"],
"odi": ["England", "India", "New Zealand", "South Africa", "Pakistan"],
"t20": ["Pakista... |
How to rename columns in Pandas DataFrame | https://www.geeksforgeeks.org/how-to-rename-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# Define a dictionary containing ICC rankings
rankings = {
"test": ["India", "South Africa", "England", "New Zealand", "Australia"],
"odi": ["England", "India", "New Zealand", "South Africa", "Pakistan"],
"t20": ["Pakistan", "India", "Australia", "England", "New ... |
#Output : col_test_1 col_odi_1 col_t20_1
| How to rename columns in Pandas DataFrame
# Import pandas package
import pandas as pd
# Define a dictionary containing ICC rankings
rankings = {
"test": ["India", "South Africa", "England", "New Zealand", "Australia"],
"odi": ["England", "India", "New Zealand", "South Africa", "Pakistan"],
"t20": ["Pakista... |
Split a column in Pandas dataframe and get part of it | https://www.geeksforgeeks.org/split-a-column-in-pandas-dataframe-and-get-part-of-it/ | import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Geek_ID": ["Geek1_id", "Geek2_id", "Geek3_id", "Geek4_id", "Geek5_id"],
"Geek_A": [1, 1, 3, 2, 4],
"Geek_B": [1, 2, 3, 4, 6],
"Geek_R": np.random.randn(5),
}
)
# Geek_A Geek_B Geek_ID Geek_R
# 0 1 1 ... |
#Output :
| Split a column in Pandas dataframe and get part of it
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Geek_ID": ["Geek1_id", "Geek2_id", "Geek3_id", "Geek4_id", "Geek5_id"],
"Geek_A": [1, 1, 3, 2, 4],
"Geek_B": [1, 2, 3, 4, 6],
"Geek_R": np.random.randn(5),
}
)
# G... |
Split a column in Pandas dataframe and get part of it | https://www.geeksforgeeks.org/split-a-column-in-pandas-dataframe-and-get-part-of-it/ | import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Geek_ID": ["Geek1_id", "Geek2_id", "Geek3_id", "Geek4_id", "Geek5_id"],
"Geek_A": [1, 1, 3, 2, 4],
"Geek_B": [1, 2, 3, 4, 6],
"Geek_R": np.random.randn(5),
}
)
# Geek_A Geek_B Geek_ID Geek_R
# 0 1 1 ... |
#Output :
| Split a column in Pandas dataframe and get part of it
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Geek_ID": ["Geek1_id", "Geek2_id", "Geek3_id", "Geek4_id", "Geek5_id"],
"Geek_A": [1, 1, 3, 2, 4],
"Geek_B": [1, 2, 3, 4, 6],
"Geek_R": np.random.randn(5),
}
)
# G... |
Split a column in Pandas dataframe and get part of it | https://www.geeksforgeeks.org/split-a-column-in-pandas-dataframe-and-get-part-of-it/ | import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Geek_ID": ["Geek1_id", "Geek2_id", "Geek3_id", "Geek4_id", "Geek5_id"],
"Geek_A": [1, 1, 3, 2, 4],
"Geek_B": [1, 2, 3, 4, 6],
"Geek_R": np.random.randn(5),
}
)
# Geek_A Geek_B Geek_ID Geek_R
# 0 1 1 ... |
#Output :
| Split a column in Pandas dataframe and get part of it
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Geek_ID": ["Geek1_id", "Geek2_id", "Geek3_id", "Geek4_id", "Geek5_id"],
"Geek_A": [1, 1, 3, 2, 4],
"Geek_B": [1, 2, 3, 4, 6],
"Geek_R": np.random.randn(5),
}
)
# G... |
Getting Unique values from a column in Pandas dataframe | https://www.geeksforgeeks.org/getting-unique-values-from-a-column-in-pandas-dataframe/ | # import pandas as pd
import pandas as pd
gapminder_csv_url = "http://bit.ly/2cLzoxH"
# load the data with pd.read_csv
record = pd.read_csv(gapminder_csv_url)
record.head() |
#Output :
| Getting Unique values from a column in Pandas dataframe
# import pandas as pd
import pandas as pd
gapminder_csv_url = "http://bit.ly/2cLzoxH"
# load the data with pd.read_csv
record = pd.read_csv(gapminder_csv_url)
record.head()
#Output :
[END] |
Getting Unique values from a column in Pandas dataframe | https://www.geeksforgeeks.org/getting-unique-values-from-a-column-in-pandas-dataframe/ | # import pandas as pd
import pandas as pd
gapminder_csv_url = "http://bit.ly/2cLzoxH"
# load the data with pd.read_csv
record = pd.read_csv(gapminder_csv_url)
print(record["continent"].unique()) |
#Output :
| Getting Unique values from a column in Pandas dataframe
# import pandas as pd
import pandas as pd
gapminder_csv_url = "http://bit.ly/2cLzoxH"
# load the data with pd.read_csv
record = pd.read_csv(gapminder_csv_url)
print(record["continent"].unique())
#Output :
[END] |
Getting Unique values from a column in Pandas dataframe | https://www.geeksforgeeks.org/getting-unique-values-from-a-column-in-pandas-dataframe/ | # import pandas as pd
import pandas as pd
gapminder_csv_url = "http://bit.ly/2cLzoxH"
# load the data with pd.read_csv
record = pd.read_csv(gapminder_csv_url)
print(record.country.unique()) |
#Output :
| Getting Unique values from a column in Pandas dataframe
# import pandas as pd
import pandas as pd
gapminder_csv_url = "http://bit.ly/2cLzoxH"
# load the data with pd.read_csv
record = pd.read_csv(gapminder_csv_url)
print(record.country.unique())
#Output :
[END] |
Getting Unique values from a column in Pandas dataframe | https://www.geeksforgeeks.org/getting-unique-values-from-a-column-in-pandas-dataframe/ | # Write Python3 code here
# import pandas as pd
import pandas as pd
gapminder_csv_url = "http://bit.ly/2cLzoxH"
# load the data with pd.read_csv
record = pd.read_csv(gapminder_csv_url)
print(pd.unique(record["continent"])) |
#Output :
| Getting Unique values from a column in Pandas dataframe
# Write Python3 code here
# import pandas as pd
import pandas as pd
gapminder_csv_url = "http://bit.ly/2cLzoxH"
# load the data with pd.read_csv
record = pd.read_csv(gapminder_csv_url)
print(pd.unique(record["continent"]))
#Output :
[END] |
Change Data Type for one or more columns in Pandas Dataframe | https://www.geeksforgeeks.org/change-data-type-for-one-or-more-columns-in-pandas-dataframe/ | # importing pandas as pd
import pandas as pd
# sample dataframe
df = pd.DataFrame(
{
"A": [1, 2, 3, 4, 5],
"B": ["a", "b", "c", "d", "e"],
"C": [1.1, "1.0", "1.3", 2, 5],
}
)
# converting all columns to string type
df = df.astype(str)
print(df.dtypes) |
#Output : Original_dtypes: | Change Data Type for one or more columns in Pandas Dataframe
# importing pandas as pd
import pandas as pd
# sample dataframe
df = pd.DataFrame(
{
"A": [1, 2, 3, 4, 5],
"B": ["a", "b", "c", "d", "e"],
"C": [1.1, "1.0", "1.3", 2, 5],
}
)
# converting all columns to string type
df = df.as... |
Change Data Type for one or more columns in Pandas Dataframe | https://www.geeksforgeeks.org/change-data-type-for-one-or-more-columns-in-pandas-dataframe/ | # importing pandas as pd
import pandas as pd
# sample dataframe
df = pd.DataFrame(
{
"A": [1, 2, 3, 4, 5],
"B": ["a", "b", "c", "d", "e"],
"C": [1.1, "1.0", "1.3", 2, 5],
}
)
# using dictionary to convert specific columns
convert_dict = {"A": int, "C": float}
df = df.astype(convert_di... |
#Output : Original_dtypes: | Change Data Type for one or more columns in Pandas Dataframe
# importing pandas as pd
import pandas as pd
# sample dataframe
df = pd.DataFrame(
{
"A": [1, 2, 3, 4, 5],
"B": ["a", "b", "c", "d", "e"],
"C": [1.1, "1.0", "1.3", 2, 5],
}
)
# using dictionary to convert specific columns
con... |
Change Data Type for one or more columns in Pandas Dataframe | https://www.geeksforgeeks.org/change-data-type-for-one-or-more-columns-in-pandas-dataframe/ | # importing pandas as pd
import pandas as pd
# sample dataframe
df = pd.DataFrame(
{
"A": [1, 2, 3, "4", "5"],
"B": ["a", "b", "c", "d", "e"],
"C": [1.1, "2.1", 3.0, "4.1", "5.1"],
}
)
# using apply method
df[["A", "C"]] = df[["A", "C"]].apply(pd.to_numeric)
print(df.dtypes) |
#Output : Original_dtypes: | Change Data Type for one or more columns in Pandas Dataframe
# importing pandas as pd
import pandas as pd
# sample dataframe
df = pd.DataFrame(
{
"A": [1, 2, 3, "4", "5"],
"B": ["a", "b", "c", "d", "e"],
"C": [1.1, "2.1", 3.0, "4.1", "5.1"],
}
)
# using apply method
df[["A", "C"]] = df... |
Change Data Type for one or more columns in Pandas Dataframe | https://www.geeksforgeeks.org/change-data-type-for-one-or-more-columns-in-pandas-dataframe/ | # importing pandas as pd
import pandas as pd
# sample dataframe
df = pd.DataFrame(
{
"A": [1, 2, 3, 4, 5],
"B": ["a", "b", "c", "d", "e"],
"C": [1.1, 2.1, 3.0, 4.1, 5.1],
},
dtype="object",
)
# converting datatypes
df = df.infer_objects()
print(df.dtypes) |
#Output : Original_dtypes: | Change Data Type for one or more columns in Pandas Dataframe
# importing pandas as pd
import pandas as pd
# sample dataframe
df = pd.DataFrame(
{
"A": [1, 2, 3, 4, 5],
"B": ["a", "b", "c", "d", "e"],
"C": [1.1, 2.1, 3.0, 4.1, 5.1],
},
dtype="object",
)
# converting datatypes
df = d... |
Change Data Type for one or more columns in Pandas Dataframe | https://www.geeksforgeeks.org/change-data-type-for-one-or-more-columns-in-pandas-dataframe/ | import pandas as pd
data = {"name": ["Aman", "Hardik", pd.NA], "qualified": [True, False, pd.NA]}
df = pd.DataFrame(data)
print("Original_dtypes:")
print(df.dtypes)
newdf = df.convert_dtypes()
print("New_dtypes:")
print(newdf.dtypes) |
#Output : Original_dtypes: | Change Data Type for one or more columns in Pandas Dataframe
import pandas as pd
data = {"name": ["Aman", "Hardik", pd.NA], "qualified": [True, False, pd.NA]}
df = pd.DataFrame(data)
print("Original_dtypes:")
print(df.dtypes)
newdf = df.convert_dtypes()
print("New_dtypes:")
print(newdf.dtypes)
#Output : Origina... |
Difference of two columns in Pandas dataframe | https://www.geeksforgeeks.org/difference-of-two-columns-in-pandas-dataframe/ | import pandas as pd
# Create a DataFrame
df1 = {
"Name": ["George", "Andrea", "micheal", "maggie", "Ravi", "Xien", "Jalpa"],
"score1": [62, 47, 55, 74, 32, 77, 86],
"score2": [45, 78, 44, 89, 66, 49, 72],
}
df1 = pd.DataFrame(df1, columns=["Name", "score1", "score2"])
print("Given Dataframe :\n", df1)
#... |
#Output :
| Difference of two columns in Pandas dataframe
import pandas as pd
# Create a DataFrame
df1 = {
"Name": ["George", "Andrea", "micheal", "maggie", "Ravi", "Xien", "Jalpa"],
"score1": [62, 47, 55, 74, 32, 77, 86],
"score2": [45, 78, 44, 89, 66, 49, 72],
}
df1 = pd.DataFrame(df1, columns=["Name", "score1", "s... |
Difference of two columns in Pandas dataframe | https://www.geeksforgeeks.org/difference-of-two-columns-in-pandas-dataframe/ | import pandas as pd
# Create a DataFrame
df1 = {
"Name": ["George", "Andrea", "micheal", "maggie", "Ravi", "Xien", "Jalpa"],
"score1": [62, 47, 55, 74, 32, 77, 86],
"score2": [45, 78, 44, 89, 66, 49, 72],
}
df1 = pd.DataFrame(df1, columns=["Name", "score1", "score2"])
print("Given Dataframe :\n", df1)
d... |
#Output :
| Difference of two columns in Pandas dataframe
import pandas as pd
# Create a DataFrame
df1 = {
"Name": ["George", "Andrea", "micheal", "maggie", "Ravi", "Xien", "Jalpa"],
"score1": [62, 47, 55, 74, 32, 77, 86],
"score2": [45, 78, 44, 89, 66, 49, 72],
}
df1 = pd.DataFrame(df1, columns=["Name", "score1", "s... |
How to drop one or multiple columns in Pandas Dataframe | https://www.geeksforgeeks.org/how-to-drop-one-or-multiple-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
"E": ["E1", "E2", "E3", "E4", "E5"],
}
# Convert the ... |
#Output : A C D E
| How to drop one or multiple columns in Pandas Dataframe
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
... |
How to drop one or multiple columns in Pandas Dataframe | https://www.geeksforgeeks.org/how-to-drop-one-or-multiple-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
"E": ["E1", "E2", "E3", "E4", "E5"],
}
# Convert the ... |
#Output : A C D E
| How to drop one or multiple columns in Pandas Dataframe
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
... |
How to drop one or multiple columns in Pandas Dataframe | https://www.geeksforgeeks.org/how-to-drop-one-or-multiple-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
"E": ["E1", "E2", "E3", "E4", "E5"],
}
# Convert the ... |
#Output : A C D E
| How to drop one or multiple columns in Pandas Dataframe
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
... |
How to drop one or multiple columns in Pandas Dataframe | https://www.geeksforgeeks.org/how-to-drop-one-or-multiple-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
"E": ["E1", "E2", "E3", "E4", "E5"],
}
# Convert the ... |
#Output : A C D E
| How to drop one or multiple columns in Pandas Dataframe
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
... |
How to drop one or multiple columns in Pandas Dataframe | https://www.geeksforgeeks.org/how-to-drop-one-or-multiple-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
"E": ["E1", "E2", "E3", "E4", "E5"],
}
# Convert the ... |
#Output : A C D E
| How to drop one or multiple columns in Pandas Dataframe
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
... |
How to drop one or multiple columns in Pandas Dataframe | https://www.geeksforgeeks.org/how-to-drop-one-or-multiple-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
"E": ["E1", "E2", "E3", "E4", "E5"],
}
# Convert the ... |
#Output : A C D E
| How to drop one or multiple columns in Pandas Dataframe
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
... |
How to drop one or multiple columns in Pandas Dataframe | https://www.geeksforgeeks.org/how-to-drop-one-or-multiple-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
"E": ["E1", "E2", "E3", "E4", "E5"],
}
# Convert the ... |
#Output : A C D E
| How to drop one or multiple columns in Pandas Dataframe
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
... |
How to drop one or multiple columns in Pandas Dataframe | https://www.geeksforgeeks.org/how-to-drop-one-or-multiple-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
"E": ["E1", "E2", "E3", "E4", "E5"],
}
# Convert the ... |
#Output : A C D E
| How to drop one or multiple columns in Pandas Dataframe
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
... |
How to drop one or multiple columns in Pandas Dataframe | https://www.geeksforgeeks.org/how-to-drop-one-or-multiple-columns-in-pandas-dataframe/ | # Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
"E": ["E1", "E2", "E3", "E4", "E5"],
}
# Convert the ... |
#Output : A C D E
| How to drop one or multiple columns in Pandas Dataframe
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
"A": ["A1", "A2", "A3", "A4", "A5"],
"B": ["B1", "B2", "B3", "B4", "B5"],
"C": ["C1", "C2", "C3", "C4", "C5"],
"D": ["D1", "D2", "D3", "D4", "D5"],
... |
Create a Pandas Series from array | https://www.geeksforgeeks.org/create-a-pandas-series-from-array/ | # importing Pandas & numpy
import pandas as pd
import numpy as np
# numpy array
data = np.array(["a", "b", "c", "d", "e"])
# creating series
s = pd.Series(data)
print(s) |
#Output :
| Create a Pandas Series from array
# importing Pandas & numpy
import pandas as pd
import numpy as np
# numpy array
data = np.array(["a", "b", "c", "d", "e"])
# creating series
s = pd.Series(data)
print(s)
#Output :
[END] |
Create a Pandas Series from array | https://www.geeksforgeeks.org/create-a-pandas-series-from-array/ | # importing Pandas & numpy
import pandas as pd
import numpy as np
# numpy array
data = np.array(["a", "b", "c", "d", "e"])
# creating series
s = pd.Series(data, index=[1000, 1001, 1002, 1003, 1004])
print(s) |
#Output :
| Create a Pandas Series from array
# importing Pandas & numpy
import pandas as pd
import numpy as np
# numpy array
data = np.array(["a", "b", "c", "d", "e"])
# creating series
s = pd.Series(data, index=[1000, 1001, 1002, 1003, 1004])
print(s)
#Output :
[END] |
Creating a Pandas Series from Dictionary | https://www.geeksforgeeks.org/creating-a-pandas-series-from-dictionary/ | # import the pandas lib as pd
import pandas as pd
# create a dictionary
dictionary = {"A": 10, "B": 20, "C": 30}
# create a series
series = pd.Series(dictionary)
print(series) |
#Output : A 10 | Creating a Pandas Series from Dictionary
# import the pandas lib as pd
import pandas as pd
# create a dictionary
dictionary = {"A": 10, "B": 20, "C": 30}
# create a series
series = pd.Series(dictionary)
print(series)
#Output : A 10
[END] |
Creating a Pandas Series from Dictionary | https://www.geeksforgeeks.org/creating-a-pandas-series-from-dictionary/ | # import the pandas lib as pd
import pandas as pd
# create a dictionary
dictionary = {"D": 10, "B": 20, "C": 30}
# create a series
series = pd.Series(dictionary)
print(series) |
#Output : A 10 | Creating a Pandas Series from Dictionary
# import the pandas lib as pd
import pandas as pd
# create a dictionary
dictionary = {"D": 10, "B": 20, "C": 30}
# create a series
series = pd.Series(dictionary)
print(series)
#Output : A 10
[END] |
Creating a Pandas Series from Dictionary | https://www.geeksforgeeks.org/creating-a-pandas-series-from-dictionary/ | # import the pandas lib as pd
import pandas as pd
# create a dictionary
dictionary = {"A": 50, "B": 10, "C": 80}
# create a series
series = pd.Series(dictionary, index=["B", "C", "A"])
print(series) |
#Output : A 10 | Creating a Pandas Series from Dictionary
# import the pandas lib as pd
import pandas as pd
# create a dictionary
dictionary = {"A": 50, "B": 10, "C": 80}
# create a series
series = pd.Series(dictionary, index=["B", "C", "A"])
print(series)
#Output : A 10
[END] |
Creating a Pandas Series from Dictionary | https://www.geeksforgeeks.org/creating-a-pandas-series-from-dictionary/ | # import the pandas lib as pd
import pandas as pd
# create a dictionary
dictionary = {"A": 50, "B": 10, "C": 80}
# create a series
series = pd.Series(dictionary, index=["B", "C", "D", "A"])
print(series) |
#Output : A 10 | Creating a Pandas Series from Dictionary
# import the pandas lib as pd
import pandas as pd
# create a dictionary
dictionary = {"A": 50, "B": 10, "C": 80}
# create a series
series = pd.Series(dictionary, index=["B", "C", "D", "A"])
print(series)
#Output : A 10
[END] |
Pandas | Basic of Time Series Manipulation | https://www.geeksforgeeks.org/pandas-basic-of-time-series-manipulation/ | import pandas as pd
from datetime import datetime
import numpy as np
range_date = pd.date_range(start="1/1/2019", end="1/08/2019", freq="Min")
print(range_date) |
#Output : DatetimeIndex(['2019-01-01 00:00:00', '2019-01-01 00:01:00', | Pandas | Basic of Time Series Manipulation
import pandas as pd
from datetime import datetime
import numpy as np
range_date = pd.date_range(start="1/1/2019", end="1/08/2019", freq="Min")
print(range_date)
#Output : DatetimeIndex(['2019-01-01 00:00:00', '2019-01-01 00:01:00',
[END] |
Pandas | Basic of Time Series Manipulation | https://www.geeksforgeeks.org/pandas-basic-of-time-series-manipulation/ | import pandas as pd
from datetime import datetime
import numpy as np
range_date = pd.date_range(start="1/1/2019", end="1/08/2019", freq="Min")
print(type(range_date[110])) |
#Output : DatetimeIndex(['2019-01-01 00:00:00', '2019-01-01 00:01:00', | Pandas | Basic of Time Series Manipulation
import pandas as pd
from datetime import datetime
import numpy as np
range_date = pd.date_range(start="1/1/2019", end="1/08/2019", freq="Min")
print(type(range_date[110]))
#Output : DatetimeIndex(['2019-01-01 00:00:00', '2019-01-01 00:01:00',
[END] |
Pandas | Basic of Time Series Manipulation | https://www.geeksforgeeks.org/pandas-basic-of-time-series-manipulation/ | import pandas as pd
from datetime import datetime
import numpy as np
range_date = pd.date_range(start="1/1/2019", end="1/08/2019", freq="Min")
df = pd.DataFrame(range_date, columns=["date"])
df["data"] = np.random.randint(0, 100, size=(len(range_date)))
print(df.head(10)) |
#Output : DatetimeIndex(['2019-01-01 00:00:00', '2019-01-01 00:01:00', | Pandas | Basic of Time Series Manipulation
import pandas as pd
from datetime import datetime
import numpy as np
range_date = pd.date_range(start="1/1/2019", end="1/08/2019", freq="Min")
df = pd.DataFrame(range_date, columns=["date"])
df["data"] = np.random.randint(0, 100, size=(len(range_date)))
print(df.head(10))
... |
Pandas | Basic of Time Series Manipulation | https://www.geeksforgeeks.org/pandas-basic-of-time-series-manipulation/ | import pandas as pd
from datetime import datetime
import numpy as np
range_date = pd.date_range(start="1/1/2019", end="1/08/2019", freq="Min")
df = pd.DataFrame(range_date, columns=["date"])
df["data"] = np.random.randint(0, 100, size=(len(range_date)))
string_data = [str(x) for x in range_date]
print(string_data[1:... |
#Output : DatetimeIndex(['2019-01-01 00:00:00', '2019-01-01 00:01:00', | Pandas | Basic of Time Series Manipulation
import pandas as pd
from datetime import datetime
import numpy as np
range_date = pd.date_range(start="1/1/2019", end="1/08/2019", freq="Min")
df = pd.DataFrame(range_date, columns=["date"])
df["data"] = np.random.randint(0, 100, size=(len(range_date)))
string_data = [str(x... |
Pandas | Basic of Time Series Manipulation | https://www.geeksforgeeks.org/pandas-basic-of-time-series-manipulation/ | import pandas as pd
from datetime import datetime
import numpy as np
range_data = pd.date_range(start="1/1/2019", end="1/08/2019", freq="Min")
df = pd.DataFrame(range_data, columns=["date"])
df["data"] = np.random.randint(0, 100, size=(len(range_data)))
df["datetime"] = pd.to_datetime(df["date"])
df = df.set_index("... |
#Output : DatetimeIndex(['2019-01-01 00:00:00', '2019-01-01 00:01:00', | Pandas | Basic of Time Series Manipulation
import pandas as pd
from datetime import datetime
import numpy as np
range_data = pd.date_range(start="1/1/2019", end="1/08/2019", freq="Min")
df = pd.DataFrame(range_data, columns=["date"])
df["data"] = np.random.randint(0, 100, size=(len(range_data)))
df["datetime"] = pd.... |
Read More | https://www.geeksforgeeks.org/how-to-get-the-daily-news-using-python/ | import requests
from bs4 import BeautifulSoup |
#Output : pip install bs4 | Read More
import requests
from bs4 import BeautifulSoup
#Output : pip install bs4
[END] |
Read More | https://www.geeksforgeeks.org/how-to-get-the-daily-news-using-python/ | url = "https://www.bbc.com/news"
response = requests.get(url) |
#Output : pip install bs4 | Read More
url = "https://www.bbc.com/news"
response = requests.get(url)
#Output : pip install bs4
[END] |
Read More | https://www.geeksforgeeks.org/how-to-get-the-daily-news-using-python/ | soup = BeautifulSoup(response.text, "html.parser")
headlines = soup.find("body").find_all("h3")
for x in headlines:
print(x.text.strip()) |
#Output : pip install bs4 | Read More
soup = BeautifulSoup(response.text, "html.parser")
headlines = soup.find("body").find_all("h3")
for x in headlines:
print(x.text.strip())
#Output : pip install bs4
[END] |
Read More | https://www.geeksforgeeks.org/how-to-get-the-daily-news-using-python/ | import requests
from bs4 import BeautifulSoup
url = "https://www.bbc.com/news"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
headlines = soup.find("body").find_all("h3")
for x in headlines:
print(x.text.strip()) |
#Output : pip install bs4 | Read More
import requests
from bs4 import BeautifulSoup
url = "https://www.bbc.com/news"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
headlines = soup.find("body").find_all("h3")
for x in headlines:
print(x.text.strip())
#Output : pip install bs4
[END] |
Read More | https://www.geeksforgeeks.org/how-to-get-the-daily-news-using-python/ | import requests
from bs4 import BeautifulSoup
url = "https://www.bbc.com/news"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
headlines = soup.find("body").find_all("h3")
unwanted = [
"BBC World News TV",
"BBC World Service Radio",
"News daily newsletter",
"Mobile app"... |
#Output : pip install bs4 | Read More
import requests
from bs4 import BeautifulSoup
url = "https://www.bbc.com/news"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
headlines = soup.find("body").find_all("h3")
unwanted = [
"BBC World News TV",
"BBC World Service Radio",
"News daily newsletter",
"M... |
Word guessing game in Python | https://www.geeksforgeeks.org/python-program-for-word-guessing-game/ | import random
# library that we use in order to choose
# on random words from a list of words
name = input("What is your name? ")
# Here the user is asked to enter the name first
print("Good Luck ! ", name)
words = [
"rainbow",
"computer",
"science",
"programming",
"python",
"mathematics",
... |
#Output : What is your name? Gautam | Word guessing game in Python
import random
# library that we use in order to choose
# on random words from a list of words
name = input("What is your name? ")
# Here the user is asked to enter the name first
print("Good Luck ! ", name)
words = [
"rainbow",
"computer",
"science",
"programming",
... |
Word guessing game in Python | https://www.geeksforgeeks.org/python-program-for-word-guessing-game/ | import random
def isword(user_word,wordly_word):
for x in user_word:
print(x,end=" ")
print()
#If alphabet present in same position green
#if alphabet present in word yellow
#if alphabet is not present black
for i in range(len(user_word)):
if user_word[i] == wordly_word[i]:
print("????",end ="")
elif us... | From code | Word guessing game in Python
import random
def isword(user_word,wordly_word):
for x in user_word:
print(x,end=" ")
print()
#If alphabet present in same position green
#if alphabet present in word yellow
#if alphabet is not present black
for i in range(len(user_word)):
if user_word[i] == wordly_word[i]:
pr... |
Hangman Game in Python | https://www.geeksforgeeks.org/hangman-game-python/ | # Python Program to illustrate# Hangman Gameimport randomfrom collections import Counter??????someWords = '''apple banana mango strawberryorange grape pineapple apricot lemon coconut watermeloncherry papaya berry peach lychee muskmelon'''??????someWords = someWords.split(' ')# randomly choose a secret word f"someWords"... |
#Output : omkarpathak@omkarpathak-Inspiron-3542:~/Documents/ | Hangman Game in Python
# Python Program to illustrate# Hangman Gameimport randomfrom collections import Counter??????someWords = '''apple banana mango strawberryorange grape pineapple apricot lemon coconut watermeloncherry papaya berry peach lychee muskmelon'''??????someWords = someWords.split(' ')# randomly choose a s... |
21 Number game in Python | https://www.geeksforgeeks.org/21-number-game-in-python/ | # Python code to play 21 Number game
# returns the nearest multiple to 4
def nearestMultiple(num):
if num >= 4:
near = num + (4 - (num % 4))
else:
near = 4
return near
def lose1():
print("\n\nYOU LOSE !")
print("Better luck next time !")
exit(0)
# checks whether the numbers... |
#Output : Player 2 is Computer. | 21 Number game in Python
# Python code to play 21 Number game
# returns the nearest multiple to 4
def nearestMultiple(num):
if num >= 4:
near = num + (4 - (num % 4))
else:
near = 4
return near
def lose1():
print("\n\nYOU LOSE !")
print("Better luck next time !")
exit(0)
# c... |
Mastermind Game using Python | https://www.geeksforgeeks.org/mastermind-game-using-python/ | import random????????????# the .randrange() function generates a# random number within the specified range.num = random.randrange(1000, 10000)??????n "Guess the 4 digit number:"))??????# condition to test equality of the# guess made. Program terminates if true.if (n == num):??????????"Great! You guessed the number in j... |
#Output : Player 1, set the number: 5672 | Mastermind Game using Python
import random????????????# the .randrange() function generates a# random number within the specified range.num = random.randrange(1000, 10000)??????n "Guess the 4 digit number:"))??????# condition to test equality of the# guess made. Program terminates if true.if (n == num):??????????"Great... |
Mastermind Game using Python | https://www.geeksforgeeks.org/mastermind-game-using-python/ | import random
# the .randrange() function generates
# a random number within the specified range.
num = random.randrange(1000, 10000)
n = int(input("Guess the 4 digit number:"))
# condition to test equality of the
# guess made. Program terminates if true.
if n == num:
print("Great! You guessed the number in just... |
#Output : Player 1, set the number: 5672 | Mastermind Game using Python
import random
# the .randrange() function generates
# a random number within the specified range.
num = random.randrange(1000, 10000)
n = int(input("Guess the 4 digit number:"))
# condition to test equality of the
# guess made. Program terminates if true.
if n == num:
print("Great! Y... |
2048 Game in Python | https://www.geeksforgeeks.org/2048-game-in-python/ | # logic.py to be
# imported in the 2048.py file
# importing random package
# for methods to generate random
# numbers.
import random
# function to initialize game / grid
# at the start
def start_game():
# declaring an empty list then
# appending 4 list each with four
# elements as 0.
mat = []
for... |
#Output : Commands are as follows : | 2048 Game in Python
# logic.py to be
# imported in the 2048.py file
# importing random package
# for methods to generate random
# numbers.
import random
# function to initialize game / grid
# at the start
def start_game():
# declaring an empty list then
# appending 4 list each with four
# elements as 0.
... |
2048 Game in Python | https://www.geeksforgeeks.org/2048-game-in-python/ | # 2048.py
# importing the logic.py file
# where we have written all the
# logic functions used.
import logic
# Driver code
if __name__ == "__main__":
# calling start_game function
# to initialize the matrix
mat = logic.start_game()
while True:
# taking the user input
# for next step
x = input... |
#Output : Commands are as follows : | 2048 Game in Python
# 2048.py
# importing the logic.py file
# where we have written all the
# logic functions used.
import logic
# Driver code
if __name__ == "__main__":
# calling start_game function
# to initialize the matrix
mat = logic.start_game()
while True:
# taking the user input
# for nex... |
Flames game in Python | https://www.geeksforgeeks.org/python-program-to-implement-simple-flames-game/ | # function for removing common characters
# with their respective occurrences
def remove_match_char(list1, list2):
for i in range(len(list1)):
for j in range(len(list2)):
# if common character is found
# then remove that character
# and return list of concatenated
... | #Input : player1 name : AJAY
player 2 name : PRIYA | Flames game in Python
# function for removing common characters
# with their respective occurrences
def remove_match_char(list1, list2):
for i in range(len(list1)):
for j in range(len(list2)):
# if common character is found
# then remove that character
# and return list... |
Pok??????mon Training | https://www.geeksforgeeks.org/python-pokemon-training-game/ | # python code to train pokemon
powers = [3, 8, 9, 7]
mini, maxi = 0, 0
for power in powers:
if mini == 0 and maxi == 0:
mini, maxi = powers[0], powers[0]
print(mini, maxi)
else:
mini = min(mini, power)
maxi = max(maxi, power)
print(mini, maxi)
# Time Complexity is O(N)... | #Input :
| Pok??????mon Training
# python code to train pokemon
powers = [3, 8, 9, 7]
mini, maxi = 0, 0
for power in powers:
if mini == 0 and maxi == 0:
mini, maxi = powers[0], powers[0]
print(mini, maxi)
else:
mini = min(mini, power)
maxi = max(maxi, power)
print(mini, maxi)
# ... |
Rock Paper Scissor game in Python | https://www.geeksforgeeks.org/python-program-implement-rock-paper-scissor-game/ | # import random module
import random
# print multiline instruction
# performstring concatenation of string
print(
"Winning rules of the game ROCK PAPER SCISSORS are :\n"
+ "Rock vs Paper -> Paper wins \n"
+ "Rock vs Scissors -> Rock wins \n"
+ "Paper vs Scissors -> Scissor wins \n"
)
while True:
p... |
#Output : Winning Rules as follows: | Rock Paper Scissor game in Python
# import random module
import random
# print multiline instruction
# performstring concatenation of string
print(
"Winning rules of the game ROCK PAPER SCISSORS are :\n"
+ "Rock vs Paper -> Paper wins \n"
+ "Rock vs Scissors -> Rock wins \n"
+ "Paper vs Scissors -> Sci... |
Taking Screenshots using pyscreenshot in Python | https://www.geeksforgeeks.org/taking-screenshots-using-pyscreenshot-in-python/ | # Program to take screenshot
import pyscreenshot
# To capture the screen
image = pyscreenshot.grab()
# To display the captured screenshot
image.show()
# To save the screenshot
image.save("GeeksforGeeks.png") |
#Output : pip install pyscreenshot | Taking Screenshots using pyscreenshot in Python
# Program to take screenshot
import pyscreenshot
# To capture the screen
image = pyscreenshot.grab()
# To display the captured screenshot
image.show()
# To save the screenshot
image.save("GeeksforGeeks.png")
#Output : pip install pyscreenshot
[END] |
Taking Screenshots using pyscreenshot in Python | https://www.geeksforgeeks.org/taking-screenshots-using-pyscreenshot-in-python/ | # Program for partial screenshot
import pyscreenshot
# im=pyscreenshot.grab(bbox=(x1,x2,y1,y2))
image = pyscreenshot.grab(bbox=(10, 10, 500, 500))
# To view the screenshot
image.show()
# To save the screenshot
image.save("GeeksforGeeks.png") |
#Output : pip install pyscreenshot | Taking Screenshots using pyscreenshot in Python
# Program for partial screenshot
import pyscreenshot
# im=pyscreenshot.grab(bbox=(x1,x2,y1,y2))
image = pyscreenshot.grab(bbox=(10, 10, 500, 500))
# To view the screenshot
image.show()
# To save the screenshot
image.save("GeeksforGeeks.png")
#Output : pip install p... |
Desktop Notifier in Python | https://www.geeksforgeeks.org/desktop-notifier-python/ | import requests
import xml.etree.ElementTree as ET
# url of news rss feed
RSS_FEED_URL = "http://www.hindustantimes.com/rss/topnews/rssfeed.xml"
def loadRSS():
"""
utility function to load RSS feed
"""
# create HTTP request response object
resp = requests.get(RSS_FEED_URL)
# return response ... |
#Output : {'description': 'Months after it was first reported, the feud between Dwayne Johnson and
| Desktop Notifier in Python
import requests
import xml.etree.ElementTree as ET
# url of news rss feed
RSS_FEED_URL = "http://www.hindustantimes.com/rss/topnews/rssfeed.xml"
def loadRSS():
"""
utility function to load RSS feed
"""
# create HTTP request response object
resp = requests.get(RSS_FEED_U... |
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