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