Update pages/Measuremen_of _central_tendency.py

pages/Measuremen_of _central_tendency.py

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+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.")