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stats_machine.py

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+# import zcount
+import csv
+import statszcw
+
+with open('dataOne.csv') as csv_file:
+    csv_reader = csv.reader(csv_file, delimiter=',')
+    line_count = 0
+    x = list()
+    y = list()
+    for row in csv_reader:
+        if line_count == 0:
+            # print(row[0])
+            line_count += 1
+        else:
+            x.append(float(row[0]))
+            y.append(float(row[1]))
+
+print(x)
+print(y)
+
+print(statszcw.zcount(x))
+print(statszcw.zcount(y))
+# Count of X
+# def stats_machine(x):
+#     zcount()

statszcw/__init__.py

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+# from statszcw.zcount import zcount
+# from statszcw.zcorr import zcorr
+# from statszcw import zmean
+# from statszcw.zmedian import zmedian
+# from statszcw.zmode import zmode
+# from statszcw.zstddev import zstddev
+# from statszcw.zstderr import zstderr
+# from statszcw.zvariance import zvariance
+
+# __all__ = ['zcount', 'zcorr', 'zmean', 'zmode', 'zmedian', 'zstddev', 'zstderr', 'zvariance']

statszcw/zcorr.py

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+from statszcw import zcount
+from statszcw import zstddev
+from statszcw import zmean
+
+def zcorr(list, list2) -> float:
+    """
+    Calculates the correlation between two lists of values
+    :param list: list of values
+    :param list2: list of values
+    :return: float rounded to 2 decimal places
+    """
+
+    sum = 0
+    for i in range(0, zcount.zcount(list)):
+        sum += ((list[i] - zmean.zmean(list)) * (list2[i]-zmean.zmean(list2)))
+    cov = sum / (zcount.zcount(list)-1)
+    corr = cov / (zstddev.zstddev(list) * zstddev.zstddev(list2))
+    return round(corr, 2)
+
+if __name__ == "__main__":
+    print(zcorr([1, 2, 3, 2, 2, 3],[1, 1, 3, 2, 1, 3]))

statszcw/zcount.py

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+def zcount(list) -> float:
+    """
+    returns the number of elements in a list
+    :param list: list of elements
+    :return: int representing number of elements in given list
+    """
+    c = 0
+    for _ in list:
+        c += 1
+    return c
+
+if __name__ == "__main__":
+    print(zcount([1,2,3,4]))
+# - zmode(list: List[]) -> float
+# - zmedian(list: List[]) -> float
+# - zvariance(list: List[]) -> float
+# - zstddev(list: List[]) -> float
+# - zstderr(list: List[]) -> float
+# - zcorr(listx: List[], listy: List[]) -> float

statszcw/zmean.py

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+from statszcw import zcount
+
+def zmean(list) -> float:
+    """
+    Returns mean of given list
+    :param list: list
+    :return: float
+    """
+    mean = sum(list) / zcount.zcount(list)
+    return float(mean)
+    # mean_list = 0
+    # for i in list:
+    #     mean_list += i
+    # mean = mean_list / zcount(list)
+    # return mean
+if __name__ == "__main__":
+    print(zmean([1, 2, 3, 4, 2, 2, 3]))

statszcw/zmedian.py

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+from statszcw import zcount
+
+def zmedian(list) -> float:
+    """
+    finds median value of given list
+    :param list: list of values
+    :return: float or int
+    """
+    list.sort()
+    if len(list) % 2 != 0:
+        return list[(int(zcount(list)/2))]
+    elif len(list) % 2 == 0:
+        return (list[(int(zcount(list)/2))] + list[(int(zcount(list)/2))-1]) / 2
+
+if __name__ == "__main__":
+    print(zmedian([1, 2, 3, 2, 2, 3]))
+# print(zmedian([1, 6, 2, 3, 7, 2, 2, 3]))
+# listo = [1, 2, 3, 2, 2, 3]
+#
+# print((listo[(int(len(listo)/2))] + listo[(int(len(listo)/2))-1])/2)

statszcw/zmode.py

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+def zmode(list) -> float:
+    """Finds mode of given list
+    :param list: list of values
+    :return: int, float, None if no mode, or list if multiple modes"""
+    # mode = 0
+    # mode_count = 0
+    for i in list:
+        mode_count = 0
+        mode = 0
+        # index = 0
+        for i in list:
+            if list.count(i) > mode_count:
+                mode_count = list.count(i)
+                mode = i
+        return mode
+
+if __name__ == "__main__":
+    print(zmode([1, 2, 3, 4, 2, 2, 3]))
+
+            # mode_count += 1
+            # index += 1
+
+

statszcw/zstddev.py

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+from statszcw import zvariance
+import math
+
+def zstddev(list) -> float:
+    """
+    Calculates standard deviation of given list
+    :param list: list of values
+    :return: float rounded to 5 decimal places
+    """
+
+    var = zvariance.zvariance(list)
+    std_dev = math.sqrt(var)
+    return std_dev
+
+if __name__ == "__main__":
+    print(zstddev([1, 2, 3, 4, 2, 2, 3]))

statszcw/zstderr.py

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+from statszcw import zstddev
+from statszcw import zcount
+import math
+
+def zstderr(list) -> float:
+    """
+    Calculates standard error of mean of given list
+    :param list: list of values
+    :return: float rounded to 5 decimal places
+    """
+    return zstddev.zstddev(list) / math.sqrt(zcount.zcount(list))
+
+if __name__ == "__main__":
+    print(zstderr([3, 5, 2, 10, 1, 3]))

statszcw/zvariance.py

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+from statszcw import zmean, zcount
+# from statszcw import zcount
+
+def zvariance(list_in) -> float:
+    """
+    Finds variance of given list
+    :param list: list of values
+    :return: float
+    """
+
+    # Amanda
+    var_top = 0
+    data_mean = zmean.zmean(list_in)
+    # print(data_mean)
+    for val in list_in:
+        diff = float(val) - data_mean
+        sq_diff = diff ** 2
+        var_top += sq_diff
+    count_val = zcount.zcount(list_in) - 1
+    var = var_top / count_val
+    return float(var)
+
+    # Derek
+    # n = zcount(list)
+    # mean = zmean(list)
+    # d = [(x - mean) ** 2 for x in list]
+    # v = sum(d) / (n - 1)
+    # return round(v, 5)
+
+    # Anusha
+    # mean = zmean.zmean(list)
+    # return sum((x - mean) ** 2 for x in list) / zcount.zcount(list)
+
+if __name__ == "__main__":
+    print(zvariance([3, 5, 2, 10, 1, 3]))