Upload SAS_to_Python_clean_v1.csv

SAS_to_Python_clean_v1.csv

@@ -0,0 +1,413 @@
+SAS Code,Converted Python Code
+"
+   
+   PROC IMPORT DATAFILE = ""/path/to/dataset.csv"" 
+   DBMS = CSV OUT = WORK.MYDATA;
+   RUN;
+   
+   ","
+   
+   import pandas as pd
+   df = pd.read_csv('/path/to/dataset.csv')
+   
+
+
+   "
+"
+   
+   PROC EXPORT DATA = WORK.MYDATA
+   OUTFILE = ""/path/to/output.csv"" 
+   DBMS = CSV;
+   RUN;
+   
+   ","
+   
+   df.to_csv('/path/to/output.csv')
+   
+
+
+   "
+"
+   
+   PROC PRINT DATA = WORK.MYDATA(OBS=5);
+   RUN;
+   
+   ","
+   
+   df.head(5)
+   
+
+
+   "
+"
+   
+   PROC CONTENTS DATA = WORK.MYDATA;
+   RUN;
+   
+   ","
+   
+   df.info()
+   
+
+
+   "
+"
+   
+   PROC SQL;
+   SELECT COUNT(*) FROM WORK.MYDATA;
+   QUIT;
+   
+   ","
+   
+   df.shape[0]
+   
+
+
+   "
+"
+   
+   PROC DATASETS LIB=WORK;
+   MODIFY MYDATA;
+   RENAME old_name = new_name;
+   QUIT;
+   
+   ","
+   
+   df.rename(columns={'old_name': 'new_name'}, inplace=True)
+   
+
+
+   "
+"
+   
+   DATA WORK.MYDATA2;
+   SET WORK.MYDATA;
+   KEEP col1 col2 col3;
+   RUN;
+   
+   ","
+   
+   df2 = df[['col1', 'col2', 'col3']]
+   
+
+
+   "
+"
+   
+   DATA WORK.MYDATA2;
+   SET WORK.MYDATA;
+   WHERE col1 > 0 AND col2 < 0;
+   RUN;
+   
+   ","
+   
+   df2 = df[(df['col1'] > 0) & (df['col2'] < 0)]
+   
+
+
+   "
+"
+   
+   DATA WORK.MYDATA2;
+   SET WORK.MYDATA;
+   new_col = col1 + col2;
+   RUN;
+   
+   ","
+   
+   df['new_col'] = df['col1'] + df['col2']
+   
+
+
+    "
+"
+    
+    PROC SORT DATA = WORK.MYDATA; 
+    BY descending col1;
+    RUN;
+    
+    ","
+    
+    df.sort_values('col1', ascending=False, inplace=True)
+    
+
+
+    "
+"
+    
+    DATA WORK.MYDATA_TOTAL;
+    MERGE WORK.MYDATA1 WORK.MYDATA2;
+    BY key_col;
+    RUN;
+    
+    ","
+    
+    df_total = pd.merge(df1, df2, on='key_col')
+    
+
+
+    "
+"
+    
+    DATA MYDATA_SQRT;
+    SET MYDATA;
+    SQRT_VAL = sqrt(VALUE);
+    RUN;
+    
+    ","
+    
+    df['sqrt_val'] = df['value'].apply(np.sqrt)
+    
+
+
+    "
+"
+    
+    PROC SQL;
+    SELECT DISTINCT COL1
+    FROM MYDATA;
+    QUIT;
+    
+    ","
+    
+    df['col1'].unique()
+    
+
+
+    "
+"
+    
+    PROC SQL;
+    SELECT COL1, COUNT(*)
+    FROM MYDATA
+    GROUP BY COL1;
+    QUIT;
+    
+    ","
+    
+    df['col1'].value_counts()
+    
+
+
+    "
+"
+    
+    DATA MYDATA;
+    SET MYDATA;
+    IF COL1 = 'OLD' THEN COL1 = 'NEW';
+    RUN;
+    
+    ","
+    
+    df['col1'].replace('OLD', 'NEW', inplace=True)
+    
+
+
+    "
+"
+    
+    PROC SQL;
+    DELETE FROM WORK.MYDATA
+    WHERE col1 < 0;
+    QUIT;
+    
+    ","
+    
+    df = df[df['col1'] >= 0]
+    
+
+
+    "
+"
+    
+    DATA WORK.MYDATA;
+    SET WORK.MYDATA;
+    DROP col1;
+    RUN;
+    
+    ","
+    
+    df.drop('col1', axis=1, inplace=True)
+    
+
+
+    "
+"
+    
+    PROC TRANSPOSE DATA=WORK.MYDATA OUT=WORK.TRANPOSED;
+    BY subject;
+    VAR scores;
+    RUN;
+    
+    ","
+    
+    transposed = df.pivot(index='subject', columns='scores')
+    
+
+
+    "
+"
+    
+    DATA MYDATA;
+    SET MYDATA;
+    IF COL1 > 0 THEN NEW_COL = 'POSITIVE';
+    ELSE IF COL1 < 0 THEN NEW_COL = 'NEGATIVE';
+    ELSE NEW_COL = 'NEUTRAL';
+    RUN;
+    
+    ","
+    
+    conditions = [(df['col1'] > 0), (df['col1'] < 0)]
+    choices = ['POSITIVE', 'NEGATIVE']
+    df['new_col'] = np.select(conditions, choices, default='NEUTRAL')
+    
+
+
+    "
+"
+    
+    PROC STDIZE DATA=WORK.MYDATA OUT=WORK.NEW_MYDATA
+    REPLEV=MISSING METHOD=MEAN;
+    RUN;
+    
+    ","
+    
+    df.fillna(df.mean(), inplace=True)
+    
+
+
+    "
+"
+    
+    PROC GLMMOD DATA=WORK.MYDATA OUTDESIGN=WORK.DESIGN;
+    CLASS COL1;
+    MODEL COL2 = COL1 / SOLUTION;
+    RUN;
+    
+    ","
+    
+    design = pd.get_dummies(df, columns=['col1'])
+    
+
+
+    "
+"
+    
+    PROC FREQ DATA=WORK.MYDATA;
+    TABLES COL1;
+    RUN;
+    
+    ","
+    
+    pd.crosstab(index=df['col1'], columns=""count"")
+    
+
+
+    "
+"
+    
+    DATA MYDATA_SUB;
+    SET MYDATA;
+    IF COL1 > 0 AND COL2 <= 50;
+    RUN;
+    
+    ","
+    
+    df_sub = df[(df['col1'] > 0) & (df['col2'] <= 50)]
+    
+
+
+    "
+"
+    
+    PROC CONTENTS DATA=MYDATA OUT=COLNAMES(KEEP=NAME);
+    RUN;
+    
+    ","
+    
+    colnames = df.columns.tolist()
+    
+
+
+    "
+"
+    
+    PROC MEANS DATA=MYDATA MEAN MIN MAX;
+    VAR COL1;
+    RUN;
+    
+    ","
+    
+    df['col1'].describe()
+    
+
+
+    "
+"
+    
+    PROC SQL;
+    SELECT A, B, MEAN(C)
+    FROM MYDATA
+    GROUP BY A, B;
+    QUIT;
+    
+    ","
+    
+    df.groupby(['A', 'B'])['C'].mean().reset_index()
+    
+
+
+    "
+"
+    
+    PROC SQL;
+    SELECT A, SUM(B)
+    FROM MYDATA
+    GROUP BY A;
+    QUIT;
+    
+    ","
+    
+    df.groupby('A')['B'].sum().reset_index()
+    
+
+
+    "
+"
+    
+    DATA MYDATA;
+    SET MYDATA;
+    COL1_NUM = INPUT(COL1, BEST.);
+    RUN;
+    
+    ","
+    
+    df['col1_num'] = df['col1'].astype(int)
+    
+
+
+    "
+"
+    
+    DATA MYDATA;
+    SET MYDATA;
+    IF MISSING(COL1) THEN COL1 = 0;
+    RUN;
+    
+    ","
+    
+    df['col1'].fillna(0, inplace=True)
+    
+
+
+    "
+"
+    
+    DATA MYDATA;
+    SET MYDATA1 MYDATA2 MYDATA3;
+    RUN;
+    
+    ","
+    
+    df = pd.concat([df1, df2, df3])
+    "