Create app.py

app.py

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+import streamlit as st 
+import numpy as np 
+import pandas as pd 
+
+st.title("PANDAS")
+st.subheader("What is pandas?")
+st.markdown("Pandas is a Python library used for working with data sets.")
+st.markdown("It has functions for analyzing, cleaning, exploring, and manipulating data.")
+st.markdown("The name Pandas has a reference to both Panel Data, and Python Data Analysis")
+st.subheader("Why Use Pandas?")
+st.markdown("Pandas allows us to analyze big data and make conclusions based on statistical theories.")
+st.markdown("Pandas can clean messy data sets, and make them readable and relevant.")
+st.markdown("Relevant data is very important in data science.")
+st.subheader("What Can Pandas Do?")
+st.markdown("Pandas gives you answers about the data. Like:")
+st.write("Is there a correlation between two or more columns?")
+st.markdown("What is average value?")
+st.markdown("Max value?")
+st.markdown("Min value?")
+st.subheader("what is cleaning data:")
+st.markdown("Pandas are also able to delete rows that are not relevant, or contains wrong values, like empty or NULL values. This is called cleaning the data.")
+st.subheader("Installation of Pandas")
+st.markdown("If you have Python and PIP already installed on a system, then installation of Pandas is very easy.")
+st.markdown("or use a python distribution that already has Pandas installed like, Anaconda, Spyder etc.")
+st.subheader("import pandas:")
+st.markdown("Once Pandas is installed, import it in your applications by adding the import keyword:")
+st.markdown("[import pandas]")
+st.markdown("Now Pandas is imported and ready to use.")
+st.subheader("Pandas as pd")
+st.markdown("Pandas is usually imported under the pd alias.")
+st.markdown("it is an alternate name of pandas")
+st.markdown("instead of writing pandas we write pd")
+st.subheader("Create an alias with the as keyword while importing:")
+st.markdown("import pandas as pd")
+st.markdown("Now the Pandas package can be referred to as pd instead of pandas.")
+st.header("Pandas Series")
+st.subheader("What is a Series?")
+st.markdown("A Pandas Series is like a column in a table.")
+st.markdown("It is a one-dimensional array holding data of any type.")
+st.subheader("Example:")
+st.markdown("Create a simple Pandas Series from a list:")
+st.markdown("import pandas as pd")
+st.markdown("a = [1, 7, 2]")
+st.markdown("myvar = pd.Series(a)")
+st.markdown("print(myvar)")
+st.subheader("Output:")
+st.markdown("0   , 1")
+st.markdown("1   , 7")
+st.markdown("2  ,  2")
+st.markdown("dtype: int64")
+st.subheader("Labels:")
+st.markdown("If nothing else is specified, the values are labeled with their index number. First value has index 0, second value has index 1 etc.")
+st.markdown("This label can be used to access a specified value.")
+st.subheader("Example:")
+st.markdown("Return the first value of the Series:")
+st.markdown("import pandas as pd")
+st.markdown("a = [1, 7, 2]")
+st.markdown("myvar = pd.Series(a)")
+st.markdown("print(myvar[0])")
+st.subheader("Output")
+st.subheader("1")
+st.subheader("Create Labels:")
+st.markdown("with the index argument, you can name your own labels.")
+st.subheader("Example:")
+st.markdown("import pandas as pd")
+st.markdown("a = [1, 7, 2]")
+st.markdown("myvar = pd.Series a, index = [x, y, z]")
+st.markdown("print(myvar)")
+st.subheader("Output:")
+st.markdown("x   , 1")
+st.markdown("y   , 7")
+st.markdown("z   ,  2")
+st.markdown("dtype: int64")
+st.markdown("When you have created labels, you can access an item by referring to the label.")
+st.subheader("Example:")
+st.markdown("Return the value of y:")
+st.subheader("import pandas as pd")
+st.markdown("a = [1, 7, 2]")
+st.markdown("myvar = pd.Series(a, index = [x, y, z])")
+st.markdown("print(myvar [y])")
+st.subheader("Output:")
+st.subheader("7")
+st.subheader("Key/Value Objects as Series")
+st.markdown("You can also use a key/value object, like a dictionary, when creating a Series.")
+st.subheader("Example:")
+st.markdown("Create a simple Pandas Series from a dictionary:")
+st.markdown("import pandas as pd")
+st.markdown("calories = day1 : 420, day2 : 380, day3 : 390")
+st.markdown("myvar = pd.Series(calories)")
+st.markdown("print(myvar)")
+st.subheader("Output:")
+st.markdown("day1    420")
+st.markdown("day2    380")
+st.markdown("day3    390")
+st.markdown("dtype: int64")
+st.markdown("Note: The keys of the dictionary become the labels.")
+st.markdown("To select only some of the items in the dictionary, use the index argument and specify only the items you want to include in the Series.")
+st.subheader("Example:")
+st.markdown("import pandas as pd")
+st.markdown("calories =  day1 : 420, day2 : 380, day3 : 390")
+st.markdown("myvar = pd.Series(calories, index = [day1, day2])")
+st.markdown("print(myvar)")
+st.subheader("Output:")
+st.markdown("day1  ,  420")
+st.markdown("day2  ,  380")
+st.markdown("dtype: int64")
+st.subheader("DataFrames:")
+st.markdown("Data sets in Pandas are usually multi-dimensional tables, called DataFrames.")
+st.markdown("Series is like a column, a DataFrame is the whole table.")
+st.subheader("Example:")
+st.markdown("Create a DataFrame from two Series:")
+st.markdown("import pandas as pd")
+st.markdown("data = {")
+st.markdown("calories : [420, 380, 390],")
+st.markdown("duration: [50, 40, 45]")
+st.markdown("}")
+st.markdown("myvar = pd.DataFrame(data)")
+st.markdown("print(myvar)")
+st.subheader("Output:")
+st.markdown("calories  duration")
+st.markdown("0   ,    420    ,    50")
+st.markdown("1   ,   380     ,    40")
+st.markdown("2   ,  390      ,    45")