dt

PYTHON/ProbeklausurWolf/A1.py

@@ -0,0 +1,22 @@
+import doctest
+
+def filter_and_scale(numbers, scale=1):
+    """
+    Eine Funktion um aus einer gegebenen Liste alle positiven Zahlen zu extrahieren, diese mit dem Skalierungsfaktor zu addieren und eine Liste mit den Werten zurückzugeben.
+    :param numbers: Liste mit allen Zahlen
+    :param scale: Skalierungsfaktor, default Wert ist 1
+    :return: Array mit nur positiven skalierten Zahlen
+
+    >>> filter_and_scale([1, -2, 3, 0, -4, 5], scale=2)
+    [2, 6, 10]
+    """
+    returnList = []
+    for elem in numbers:
+        if elem > 0:
+            returnList.append(elem * scale)
+
+    return returnList
+
+
+if __name__ == "__main__":
+    doctest.testmod()

PYTHON/ProbeklausurWolf/A3.py

@@ -0,0 +1,17 @@
+
+F = lambda x: x*x
+
+print(F(5))
+
+t = (1,2,3,7)
+
+print(type(t))
+
+u = list(t)
+
+print(type(u))
+
+print(u[-1])
+
+# for idx, x in enumerate(t):
+#     print(idx, x)

PYTHON/ProbeklausurWolf/A4.py

@@ -0,0 +1,22 @@
+import numpy as np
+
+d=np.load("npdata12.npy")
+print(d)
+print("\n\nG1")
+
+g1 = d[d[:, 0] == 1][:,1]
+print(g1.mean())
+
+print(g1.shape)
+
+# print(head(d))
+
+
+
+M = np.array([[1,2,3], [4,5,6]])
+
+r = M[:,[1,0,2,1]]
+
+# print(r)
+
+

PYTHON/daten.csv

@@ -0,0 +1,11 @@
+ID;Alter;Geschlecht;Blutgruppe;Raucher;Größe;Chars
+10;1;W;6;nein;168;1
+10;17;W;6;nein;168;2
+20;5;M;0;ja;175;3
+30;24;W;1;ja;176;4
+40;22;D;2;ja;177;5
+50;23;M;3;nein;178;6
+60;6;D;4;ja;178;5
+70;23;M;5;nein;170;6
+75;6;W;6;ja;179;0
+90;23;M;7;ja;179;0

PYTHON/matplot.py

@@ -0,0 +1,28 @@
+import matplotlib.pyplot as plt
+
+year = [1800, 1850,  1900,  1950,  1970,  1990,  2010,  2100]
+pop =  [1.0,  1.262, 1.650, 2.519, 3.692, 5.263, 6.972, 10.85]
+values = [0,0.6,1.4,1.6,2.2,2.5,2.6,3.2,3.5,3.9,4.2,6]
+
+# plt.scatter(year, pop)
+
+# # plt.plot(year, pop)
+
+
+# plt.xlabel('Year')
+# plt.ylabel('Population')
+# plt.title('World Population')
+
+# plt.yticks([0, 2, 4, 6, 8, 10],
+# ['0', '2 Mrd', '4 Mrd',
+# '6 Mrd', '8 Mrd', '10 Mrd'])
+
+# plt.show()
+# plt.figure()
+
+f, ax = plt.subplots(1,2, figsize=(3,1))
+ax[0].plot(year, pop)
+ax[1].hist(values, bins=3)
+
+plt.tight_layout()
+plt.show()

PYTHON/nastja.py

@@ -0,0 +1,14 @@
+import numpy as np
+
+e = np.array([[1,2,3], [2,3,4]])
+
+
+
+s = "Hallo"
+
+for x in dir(e):
+    print(x)
+
+
+
+print(s.lower())

PYTHON/p1.py

@@ -0,0 +1,97 @@
+import numpy as np
+
+s = {1,2,3}
+sn = np.array(s)
+
+print(sn)
+print(type(sn))
+
+l = [1,2,3]
+ln = np.array(l)
+
+print(ln)
+print(type(ln))
+
+t = (1,2,3)
+tn = np.array(t)
+
+print(tn)
+print(type(tn))
+
+s = "Hallo"
+print(s[2:])
+
+print(s[:-2])
+
+
+a = [1,2,3]
+c = a 
+b = a[:]
+a[0] = 6
+
+print(f"A: {a}")
+print(f"B: {b}")
+print(f"C: {c}")
+print("\n")
+
+a = np.array([7,3,1])
+
+print(a)
+a += [1,2,3]
+print(a)
+
+print("\n")
+
+b = np.array([[1,2], [2,3], [4,5]])
+
+print(b)
+print("\n")
+
+
+inner = [[1,2,0], [0,3,4]]
+c = np.array([inner, inner])
+print(c.ndim)
+print(c.shape)
+print("\n")
+
+
+d = np.array([1,2])
+print(d)
+print(d.T)
+print("\n")
+
+
+M = np.array([[1,2,3], [2,3,4], [3,4,5]])
+N = M.copy()
+print(M)
+print("")
+print(f"Test:")
+print(M[:,1:3])
+print(M[:, [1,2,1]])
+
+MSpalte = M[:, 0:2]
+MZeile = M[:, [0,2,0]]
+print(f"Spalte:\n {MSpalte}")
+print(f"Zeile:\n {MZeile}")
+print("\n")
+
+M = np.array([[1,2,3], [2,3,4], [3,4,5]])
+MCon = M > 2
+print(f"Con:\n {MCon}")
+M[M>2]=17
+print(M)
+print("\n")
+
+print(N)
+print("")
+new = (N>3) & (N > 3)
+print(new)
+
+print((N > 2) & (N < 5))
+print("\n")
+
+
+vec1 = np.array([1,2,3])
+vec2 = np.array([2,3,4])
+
+print(np.matmul(vec1, vec2))

PYTHON/panda.py

@@ -0,0 +1,66 @@
+import pandas as pd
+
+df = pd.read_csv("daten.csv",
+delimiter=";",
+na_values=["."],
+index_col="ID",
+encoding="utf-8")
+
+
+df2 = df.sort_values(["Größe", "ID"])
+
+# df3 = df.drop([80,90])
+# df4 = df.drop(columns=["Raucher", "Blutgruppe"])
+
+
+df3 = df2[df2["Größe"] > 175]
+
+# elems = df2.loc[[10], "Blutgruppe":"Größe"]
+# elems = df2.iloc[0:3, 0:2]
+# elems = df2.loc[20:80,"Geschlecht":"Raucher"]
+
+print(df2)
+
+# print("")
+# print(f"Type: {type(elems)}")
+# print(elems)
+
+# print(df2.index)
+# df5 = df2.set_index("Alter", append=True)
+df5 = df2.set_index(["Alter", "Geschlecht"], append=True)
+# print(df5)
+# print(df5.index)
+
+rowsIter = df.iterrows()
+colsIter = df.items()
+
+# print("Befor row loop:\n")
+# for rowLabel, row in rowsIter:
+#     print(f"Label: {type(rowLabel)}, row: {type(row)}")
+#     print(f"{rowLabel}:\n{row}\n")
+
+# print("Befor col loop:\n")
+# for colLabel, col in colsIter:
+#     print(f"Label: {type(colLabel)}, Col: {type(col)}")
+#     print(f"{colLabel}:\n{col}\n")
+
+# print("\n")
+# nextCol = next(colsIter)
+# nextColLabel = nextCol[0]
+# nextColSeries = nextCol[1]
+
+# print(f"Label: {nextColLabel}")
+# print(f"Series:\n{nextColSeries}")
+
+# seriesIter = nextColSeries.items()
+# nextElem = next(seriesIter)
+
+# print(nextElem)
+print(df2)
+df7 = df2.fillna(0)
+# print(df7)
+
+print(df)
+meanRet = df.max()
+print(type(meanRet))
+print(meanRet)

PYTHON/pandas2.py

@@ -0,0 +1,80 @@
+import pandas as pd
+import numpy as np
+
+df = pd.read_csv("daten.csv",
+delimiter=";",
+na_values=["."],
+index_col="ID",
+encoding="utf-8")
+
+meanRet = df.max()
+# print(type(meanRet))
+# print(meanRet)
+
+onlyNumsDf = df[["Alter", "Größe"]]
+# print(onlyNumsDf.mean())
+
+df["Alter2"] = df["Alter"] * 2
+# print(df)
+
+onlyAlter = df["Alter"]
+alterMean = onlyAlter.max()
+# print(type(alterMean))
+# print(alterMean)
+
+charMap = {
+    0: "",
+    1: "A",
+    2: "M",
+    3: "O",
+    4: "G",
+    5: "U",
+    6: "S",
+}
+
+def intToChar(col):
+    retString = ""
+    for num in col:
+        char = charMap.get(num, " ")
+        retString += char
+    
+    return retString
+
+
+def timesTwo(num):
+    return num*2
+
+charNums = df[["Alter", "Chars"]]
+print(charNums)
+print("")
+def map_numbers_to_string(column):
+    return "".join(charMap[num] for num in column)
+
+
+print(charNums.apply(intToChar, axis=0))
+print("")
+print(charNums.apply(timesTwo, axis=0))
+# print(charNums.apply(intToChar))
+print("")
+alterSeries = df[["Alter"]]
+print(alterSeries.apply(np.sum, axis=0))
+
+print(df)
+raucherGeschlechtDf = df.loc[10:70 ,["Raucher","Geschlecht"]]
+geschlechtGrößeDf = df.loc[50:90, ["Geschlecht", "Größe"]]
+alterBlutgruppeDf = df.loc[50:90 ,["Alter", "Blutgruppe"]]
+print("\nRaucher Geschlecht: ")
+print(raucherGeschlechtDf)
+print("\nGeschlecht Größe: ")
+print(geschlechtGrößeDf)
+
+# print("\nMerge: ")
+# res = raucherGeschlechtDf.merge(geschlechtGrößeDf, on="Geschlecht")
+
+print("\nGroup: ")
+res = raucherGeschlechtDf.groupby(geschlechtGrößeDf, by="Geschlecht")
+
+print(res)
+
+
+