Update app.py

app.py

@@ -45,248 +45,6 @@ st.subheader("**2.1 Descriptive Statistics**")
 st.markdown("""This Descriptive Statistics describe the main feature of data. This
 descriptive statistics can be performed on sample data as well as population data. Some of
 the key points of descriptive statistics are stated below.\n KEY COCEPTS\n * Measurement of Central Tendency which involves finding Mean, Median, and Mode.\n * Measurement of Dispersion which involves finding Range, Variance and Standard Deviation.\n * Distribution which gives how frequently the data is occurring some of examples of distribution are Gaussian, Random, and Normal distribution""")
-st.subheader("Measure Of Central Tendency",divider=True)
-st.markdown("""The measure of central tendency is used to find the central average value of the data.The central tendency can be computed by 
-useing three ways \n * Mode \n * Median \n * Mean""")
-st.subheader("MODE",divider=True)
-st.markdown("""Mode will be giving the centeral tendency based on most frequently occuring data.The major drawback of mode is its frequecy baised it 
-mostly focus on the data which is occuring most times.Here in this mode we might come across some situation's like """)
-st.markdown(''':violet[No_Mode] \n  Let's understand why this situation raises for example let's take list of numbers [1,2,3,4,5] here we don't have 
-frequency of numbers repeating in this senario we will come accross No_Mode situaton.
-''')
-st.markdown(''':violet[Uni_Mode] \n  Let's understand why this situation raises for example let's take list of numbers [1,1,1,2,3,4,5]. here by 
-checking the list it will tend to know that the frequency of number 1 is more and it returns the value 1 as output.
-''')
-st.markdown(''':violet[Bi_Mode] \n  Let's understand why this situation raises for example let's take list of numbers [1,1,2,2,3,4,5]. here by
-checking the frequency in list we come across a situtaion where we will find two maximun frequecy repeated value hence the output will be Bi_Mode.
-''')
-st.markdown(''':violet[Tri_Mode] \n  Let's understand why this situation raises for example let's take list of numbers [1,1,2,2,3,3,4,5]. here by
-checking the frequency in list we come across a situtaion where we will find three maximun frequecy repeated value hence the output will be Tri_Mode.
-''')
-st.markdown(''':violet[Multi_Mode] \n  Let's understand why this situation raises for example let's take list of numbers [1,1,2,2,3,3,4,4,5]. here by
-checking the frequency in list we come across a situtaion where we will find more than three maximun frequecy repeated value hence the output will be Multi_Mode.
-''')
-st.title("Calculate Mode")
-def mode(*args):
-    list1 = list(args)
-    dict1 = {}
-    dict2 = {}
-    set1 = set(list1)
-    for j in set1:
-        dict1[j] = list1.count(j)
-    max_value = max(dict1.values())
-    count = [key for key, value in dict1.items() if value == max_value]
-    if max_value == 1:
-        return 'no mode'
-    elif len(count) == len(set1):
-        return 'no mode'
-    elif len(count) == 1:
-        dict2[count[0]] = dict1.get(count[0])
-        return dict2
-    elif len(count) == 2:
-        return 'bi mode'
-    elif len(count) == 3:
-        return 'tri mode'
-    else:
-        return 'multimode'
-numbers_input = st.text_input("Enter a list of numbers separated by commas (e.g., 1, 2, 2, 3, 4):")
-
-if numbers_input:
-    try:
-        list1 = list(map(int, numbers_input.split(',')))
-        result = mode(*list1)
-        st.write("Mode result:", result)
-    except ValueError:
-        st.write("Please enter a valid list of numbers separated by commas.")
-st.subheader("Median",divider=True)
-st.markdown("""Median will also be giving the central tendency.But the major drawback of median is it prior foucus will be on the central value.
-In order to find the mean first we have to sort the give list and based on the length of the list the formula are changed""")
-st.subheader("Median Formula for Odd Number of Observations")
-st.latex(r'''
-\text{Median} = X_{\left(\frac{n+1}{2}\right)}
-''')
-st.subheader("Median Formula for Even Number of Observations")
-st.latex(r'''
-\text{Median} = \frac{X_{\left(\frac{n}{2}\right)} + X_{\left(\frac{n}{2}+1\right)}}{2}
-''')
-def median(list1):
-    list1.sort()
-    length = len(list1)
-    if length % 2 == 0:
-        mid1 = length // 2 - 1
-        mid2 = length // 2
-        return (list1[mid1] + list1[mid2]) / 2
-    else:
-        mid = length // 2
-        return list1[mid]
-st.title("Calculate Median")
-numbers_input_1 = st.text_input("Enter a list of numbers separated by commas (e.g., 1, 2, 3, 4, 5):", key="numbers_input_1")
-if numbers_input_1:
-    parts = numbers_input_1.split(',')
-    list1 = []
-    
-    for num in parts:
-        num = num.strip()  
-        if num.isdigit():
-            list1.append(int(num))
-
-    if list1:  
-        result = median(list1)
-        st.write("Median result:", result)
-    else:
-        st.write("No valid numbers provided.")
-st.subheader("Mean",divider=True)
-st.markdown("""
-Mean is one of the beautiful measurement of central tendency it invovles all the data present in it.The only drawback of mean is it is 
-effected by outliers.Based on the data we will compute the mean in three types""")
-st.subheader("Arthmetic Mean",divider=True)
-st.markdown("""Arthmetic Mean is used on data which have \n * Interval and Ratio Data \n * Symmetric Distributions \n * Data Without Outliers
-""")
-st.subheader("Population Mean Formula")
-st.latex(r'''
-    \mu = \frac{1}{N} \sum_{i=1}^{N} x_i
-''')
-st.subheader("Sample Mean Formula")
-st.latex(r'''
-    \bar{x} = \frac{1}{n} \sum_{i=1}^{n} x_i
-''')
-def arthamatic_mean(list1):
-  sum=reduce(lambda x,y: x+y,list1)
-  return sum/len(list1)
-st.title("Calculate Arthmetic_Mean")
-numbers_input_2 = st.text_input("Enter a list of numbers separated by commas (e.g., 1, 2, 3, 4, 5):", key="numbers_input_2")
-if numbers_input_2:
-    parts=numbers_input_2.split(",")
-    list1=[]
-    for i in parts:
-        i = i.strip()
-        if i.isdigit():
-            list1.append(int(i))
-    if list1:
-        result=arthamatic_mean(list1)
-        st.write("Arthmetic_Mean",result)
-    else:
-        st.write("No valid numbers provided.")
-st.subheader("Geometric Mean",divider=True)
-st.markdown("""Geometric Mean is used on data which have \n * Multiplicative Data \n * Percentages and Rates \n * Normalized Data
-""")
-st.subheader("Geometric Mean for Population")
-st.latex(r'''
-    \text{GM}_{\text{population}} = \left( \prod_{i=1}^{N} x_i \right)^{\frac{1}{N}}
-''')
-st.subheader("Geometric Mean for Sample")
-st.latex(r'''
-    \text{GM}_{\text{sample}} = \left( \prod_{i=1}^{n} x_i \right)^{\frac{1}{n}}
-''')
-def geometric_mean(list1):
-  mul=reduce(lambda x,y: x*y,list1)
-  return round(mul**(1/len(list1)),2)
-st.title("Calculate Geometric_Mean")
-numbers_input_3 = st.text_input("Enter a list of numbers separated by commas (e.g., 1, 2, 3, 4, 5):", key="numbers_input_3")
-if numbers_input_3:
-    parts=numbers_input_3.split(",")
-    list1=[]
-    for i in parts:
-        i = i.strip()
-        if i.isdigit():
-            list1.append(int(i))
-    if list1:
-        result=geometric_mean(list1)
-        st.write("Geometric_Mean",result)
-    else:
-        st.write("No valid numbers provided.")
-st.subheader("Harmonic Mean",divider=True)
-st.markdown("""Harmonic Mean is used on data which have \n *  Rates and Ratios  \n *  Data with Reciprocal Relationships
-""")
-st.subheader("Harmonic Mean for Population")
-st.latex(r'''
-    \text{HM}_{\text{population}} = \frac{N}{\sum_{i=1}^{N} \frac{1}{x_i}}
-''')
-st.subheader("Harmonic Mean for Sample")
-st.latex(r'''
-    \text{HM}_{\text{sample}} = \frac{n}{\sum_{i=1}^{n} \frac{1}{x_i}}
-''')
-def harmonic_mean(list1):
-  sum=reduce(lambda x,y: x+1/y,list1)
-  return round(len(list1)/sum,2)
-st.title("Calculate Harmonic_Mean")
-numbers_input_4 = st.text_input("Enter a list of numbers separated by commas (e.g., 1, 2, 3, 4, 5):", key="numbers_input_4")
-if numbers_input_4:
-    parts=numbers_input_4.split(",")
-    list1=[]
-    for i in parts:
-        i = i.strip()
-        if i.isdigit():
-            list1.append(int(i))
-    if list1:
-        result=harmonic_mean(list1)
-        st.write("Geometric_Mean",result)
-    else:
-        st.write("No valid numbers provided.")
-st.subheader("Measure Of Disperssion ",divider=True)
-st.markdown("""Measure Of Disperssion will give spread of our collected data around the central value.It's classifed into two types
-""")
-st.markdown(''':violet[Absolute Measure] \n absolute will give the spread of data in one unit.for example if the given data is in 'cm'
-the output will be in cm''')
-st.markdown(''':violet[Relative Measure] \n  Relative will be free from unit's''')
-st.header("**Absolute Measure**")
-st.subheader("Range",divider=True)
-st.subheader("Quartile Deviation",divider=True)
-st.subheader("Varience",divider=True)
-st.subheader("Standard Deviation",divider=True)
-st.header("**Relative Measure**")
-st.subheader("Coefficent Of Range",divider=True)
-st.subheader("Coefficent Of Quartile Deviation",divider=True)
-st.subheader("Coefficent Of Varience",divider=True)
-st.subheader("Coefficent Of Standard Deviation",divider=True)
-st.markdown(''':orange[**Range**] is one of the measure to find the disperssion.But is not at all mostly used beause it don't focus on the entire data.
-''')
-st.subheader("Absolute Range")
-st.latex(r'''
-    \text{Absolute Range} = \text{Maximum Value} - \text{Minimum Value}
-''')
-st.subheader("Relative Range")
-st.latex(r'''
-    \text{Relative Range} = \frac{\text{Absolute Range}}{\text{Mean}} \times 100
-''')
-st.markdown(''':orange[**Quartile Deviation**] is one of the measure to find the disperssion.In this type the data is divided into 4 equal parts.
-It will mostly focus on the central data.
-''')
-st.subheader("Absolute Quartile Deviation")
-st.latex(r'''
-    QD = \frac{Q3 - Q1}{2}
-''')
-st.subheader("Relative Quartile Deviation")
-st.latex(r'''
-    \text{Relative QD} = \frac{Q3 - Q1}{Q3 + Q1} \times 100
-''')
-st.markdown(''':orange[**Varience**] is one of the measure to find the disperssion.It is one of the best measure to find the disperssion.The only
-drawback is when in Varience is in order to overcome negitive value we square them thus the distance is doubled
-''')
-st.subheader("Absolute Variance")
-st.latex(r'''
-    \text{Var} = \frac{1}{N} \sum_{i=1}^{N} (x_i - \bar{x})^2
-''')
-st.subheader("Relative Variance")
-st.latex(r'''
-    \text{Relative Var} = \frac{\text{Var}}{\bar{x}} \times 100
-''')
-st.markdown(''':orange[**Standard Deviation**] is one of the measure to find the disperssion.It is one of the best measure to find the disperssion.It over comes the 
-disadvantage occured in varience by square rooting it.
-''')
-st.subheader("Absolute Standard Deviation")
-st.latex(r'''
-    \sigma = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (x_i - \bar{x})^2}
-''')
-st.subheader("Relative Standard Deviation")
-st.latex(r'''
-    \text{Relative SD} = \frac{\sigma}{\bar{x}}
-''')
-st.subheader("Distribution",divider=True)
-st.markdown(''':blue[**Distribution**] is a measure will will tell how the shape of data or in which shape the data is spread.It will help in 
-analysis.There are few types of distribution \n *  Normal Distribution \n * Uniform Distribution \n * Binomial Distribution \n * Poisson Distribution
-\n * Exponential Distribution \n * Chi-Square Distribution \n * T-Distribution
-''')
 st.subheader("**2.2 Inferential Statistics**")
 st.markdown("""This Inferential Statistics will describe the population based
 on a sample data. This statistics will give predictions about a population based on sample.