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How to use test_rename_columns method in pandera

Best Python code snippet using pandera_python

testpandaszyz.py

Source:testpandaszyz.py

...54    df_new = df["Surname_Age"].str.split("_", expand =True)55    print(df_new)56#8. åæ®µçå½å57#æä¸¤ç§æ¹å¼ä¸ç§æ¯ä½¿ç¨rename()å½æ°, å¦ä¸ç§æ¯ç´æ¥è®¾ç½®columnsåæ°58def test_rename_columns():59    df = pd.DataFrame({"ID": [100000,100101,100201],"Surname_Age": ["Zhao_23","Qian_33","Sun_28" ]})60    print(df)61    print("ç¬¬ä¸ç§æ¹æ³ä½¿ç¨rename()å½æ°")62    df_new = df["Surname_Age"].str.split("_", expand =True).rename(columns={0: "Surname", 1: "Age"})63    print(df_new)64    print("ç¬¬äºç§æ¹æ³ç´æ¥è®¾ç½®columnsåæ°")65    df_new = df["Surname_Age"].str.split("_", expand =True)66    df_new.columns = ["Surname","Age"]67    print(df_new)68#9. åæ®µçåå¹¶69#ä½¿ç¨merge()å½æ°å¯¹åæ®µè¿è¡åå¹¶æä½.70def test_merge():71    df = pd.DataFrame({"ID": [100000,100101,100201],"Surname_Age": ["Zhao_23","Qian_33","Sun_28" ]})72    print(df)73    df_new = df["Surname_Age"].str.split("_", expand =True)74    df_new.columns = ["Surname","Age"]75    print(df_new)76    print("ä½¿ç¨mergeå½æ°å¯¹ä¸¤è¡¨çåæ®µè¿è¡åå¹¶æä½.")77    print(pd.merge(df, df_new, left_index =True, right_index=True))78#10. åæ®µçå é¤79#å©ç¨drop()å½æ°å¯¹åæ®µè¿è¡å é¤.80def test_drop():81    df = pd.DataFrame({"ID": [100000,100101,100201],"Surname_Age": ["Zhao_23","Qian_33","Sun_28" ]})82    print(df)83    df_new = df["Surname_Age"].str.split("_", expand =True)84    df_new.columns = ["Surname","Age"]85    print(df_new)86    df_mer= pd.merge(df, df_new, left_index =True, right_index=True)87    print(df_mer)88    print("drop()å é¤åæ®µ,ç¬¬ä¸ä¸ªåæ°æè¦å é¤çåæ®µ,axis=1è¡¨ç¤ºåæ®µæå¨å,inplaceä¸ºTrueè¡¨ç¤ºå¨å½åè¡¨æ§è¡å é¤.")89    df_mer.drop("Surname_Age", axis = 1, inplace =True)90    print(df_mer)91#11. è®°å½çæ½å92def test_chouqu():93    print("1) å³ç³»è¿ç®: df[df.åæ®µå å³ç³»è¿ç®ç¬¦ æ°å¼], æ¯å¦æ½åå¹´é¾å¤§äº30å²çè®°å½.")94    df = pd.DataFrame({"ID": [100000,100101,100201],"Surname_Age": ["Zhao_23","Qian_33","Sun_28" ]})95    print(df)96    df_new = df["Surname_Age"].str.split("_", expand =True)97    print(df_new)98    df_new.columns = ["Surname","Age"]99    print(df_new)100    df_mer= pd.merge(df, df_new, left_index =True, right_index=True)101    print(df_mer)102    df_mer.drop("Surname_Age", axis = 1, inplace =True)103    print(df_mer)104    print("å°Ageåæ®µæ°æ®ç±»åè½¬åä¸ºæ´å")105    df_mer["Age"] = df_mer["Age"].astype(int)106    print(df_mer)107    print("æ½åAgeä¸å¤§äº30çè®°å½")108    df_mer[df_mer.Age > 30]109    print(df_mer)110    print("2) èå´è¿ç®: df[df.åæ®µå.between(s1, s2)], æ³¨ææ¢åå«s1ååå«s2, æ¯å¦æ½åå¹´é¾å¤§äºçäº23å°äºçäº28çè®°å½. ")111    df_mer[df_mer.Age.between(23,28)]112    print(df_mer)113    print("3) é»è¾è¿ç®: ä¸(&) æ(|) é(not)")114    print("æ¯å¦ä¸é¢çèå´è¿ç®df_mer[df_mer.Age.between(23,28)]å°±çåäºdf_mer[(df_mer.Age >= 23) & (df_mer.Age <= 28)]")115    df_mer[(df_mer.Age >= 23 ) & (df_mer.Age <= 28)]116    print(df_mer)117    print("4) åç¬¦å¹é: df[df.åæ®µå.str.contains(\"åç¬¦\", case = True, na =False)] contains()å½æ°ä¸case=Trueè¡¨ç¤ºåºåå¤§å°å, é»è®¤ä¸ºTrue; na = Falseè¡¨ç¤ºä¸å¹éç¼ºå¤±å¼.")118    df = pd.DataFrame({"ID": [100000,100101,100201],"Surname_Age": ["Zhao_23","Qian_33","Sun_28"],"SpouseAge":[np.NaN,"32",np.NaN]})119    print(df)120    print("å¹éSpouseAgeä¸åå«2çè®°å½")121    df[df.SpouseAge.str.contains("2",na = False)]122    print(df)123    print("5) ç¼ºå¤±å¼å¹é:df[pd.isnull(df.åæ®µå)]è¡¨ç¤ºå¹éè¯¥åæ®µä¸æç¼ºå¤±å¼çè®°å½.")124    df = pd.DataFrame({"ID": [100000,100101,100201],"Surname_Age": ["Zhao_23","Qian_33","Sun_28"],"SpouseAge":[np.NaN,"32",np.NaN]})125    print(df)126    print("å¹éSpouseAgeä¸æç¼ºå¤±å¼çè®°å½")127    df[pd.isnull(df.SpouseAge)]128    print(df)129#12.è®°å½çåå¹¶130def test_concat():131    #ä½¿ç¨concat()å½æ°å¯ä»¥å°ä¸¤ä¸ªæèå¤ä¸ªæ°æ®è¡¨çè®°å½åå¹¶ä¸èµ·, ç¨æ³: pandas.concat([df1, df2, df3.....])132    df1 = pd.DataFrame({"ID": ["A10006","A10001"],"Salary": [12000, 20000]})133    df2 = pd.DataFrame({"ID": ["A10008"], "Salary": [10000]})134    print("ä½¿ç¨concat()å½æ°å°df1ä¸df2çè®°å½è¿è¡åå¹¶")135    print(pd.concat([df1, df2]))136#13.	 ç®åè®¡ç®æ°å»ºä¸ä¸ªæ°æ®è¡¨df137def test_newdataframe():138    df = pd.DataFrame({"å°åº": ["Aåº","Båº","Cåº"],139    "ååå¹´éé": [3500, 4500,3800],140    "ååå¹´éé": [3000, 6000,5000],141    "åä»·": [10, 18, 15]})142    print(df)143test_drop_duplicates()144test_fillna()145test_dropna()146test_dtypes()147test_astype()148test_slice()149test_split()150test_rename_columns()151test_merge()152test_drop()153test_chouqu()154test_concat()155test_newdataframe()156#xuanxiang={"1":"éå¤å¼çå¤ç","2":"è¡¥é½ç¼ºå¤±å¼","3":"å é¤ç¼ºå¤±å¼","4":"æ¥çæ°æ®ç±»å","5":"ä¿®æ¹æ°æ®ç±»å","6":"åæ®µçæ½å",157           #"7":"åæ®µçæå","8":"åæ®µçå½å","9":"åæ®µçåå¹¶","10":"åæ®µçå é¤","11":"è®°å½çæ½å","12":"è®°å½çåå¹¶","13":"ç®ååå»ºæ°æ®è¡¨"}158#print(xuanxiang)159#c=input("è¾å¥ç¬¬å ä¸ªæµè¯\n")160#print(c)161#if c=='1':162#    test_drop_duplicates()163#elif c=='2':164#    test_fillna()165#elif c=='3':166#    test_dropna()167#elif c=='4':168#    test_dtypes()169#elif c=='5':170#    test_astype()171#elif c=='6':172#    test_slice()173#elif c=='7':174#    test_split()175#elif c=='8':176#    test_rename_columns()177#elif c=='9':178#    test_merge()179#elif c=='10':180#    test_drop()181#elif c=='11':182#    test_chouqu()183#elif c=='12':184#    test_concat()185#elif c=='13':186#    test_newdataframe()187#else:...

testrenamefunc.py

Source:testrenamefunc.py

...16        :return: sparkdataframe17        """18    df_renamed = df.select([F.col(c).alias(col_rename.get(c, c)) for c in df.columns])19    return df_renamed20def test_rename_columns():21    df = spark.read.format("csv").option("header", "true").option('delimiter', ',').load("data/Input/dataset_one.csv")22    col_rename = {"id": "client_identifierx"}23    source_df = rename_columns(df, col_rename)24    print("ashok:" + str(source_df.columns))25    # Create Schema26    schema = StructType([27        StructField('client_identifier', StringType(), True),28        StructField('first_name', StringType(), True),29        StructField('last_name', StringType(), True),30        StructField('email', StringType(), True),31        StructField('country', StringType(), True)32    ])33    expected_df = spark.read.csv(path="data/Input/dataset_one.csv", schema=schema, header=True)34    expected_df.printSchema()35    assert_df_equality(source_df, expected_df)...

test_utils.py

Source:test_utils.py

...14tbl = QTable([[2.0, 5.0], ["x", "y"]], names=("a", "b"))15##############################################################################16# CODE17##############################################################################18def test_rename_columns():19    """Test `~astronat.utils.table.utils.rename_columns`."""20    utils.rename_columns(table=tbl, rename={"a": "A"})21    assert tbl.colnames == ["A", "b"]22    utils.rename_columns(table=tbl, rename={"A": "a"})23    assert tbl.colnames == ["a", "b"]24# /def25# -------------------------------------------------------------------26def test_cast_columns():27    """Test `~astronat.utils.table.utils.rename_columns`."""28    utils.cast_columns(table=tbl, recast={"a": lambda x: x * u.km})29    assert all(tbl["a"] == [2.0, 5.0] * u.km)30# /def31##############################################################################32# END

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