Neuer Kurzcode für Microsoft Excel

VLOOKUP: =VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])
INDEX/MATCH: =INDEX(range, MATCH(lookup_value, lookup_array, [match_type])
PivotTable: =SUMIFS(sum_range, criteria_range, criteria)
Conditional Formatting: =A1>average(A:A)
Chart creation: =SERIES(name, categories, values)
Data Analytics/Science (Python):
Dataframe creation: df = pd.DataFrame({'column1': [1, 2, 3], 'column2': [4, 5, 6]})
Data merging: pd.merge(df1, df2, on='common_column')
GroupBy: df.groupby('column').sum()
Data visualization: plt.plot(df['column'])
Machine Learning: from sklearn.linear_model import LinearRegression; model = LinearRegression()
SQL:
Data insertion: INSERT INTO table (column1, column2) VALUES ('value1', 'value2');
Data update: UPDATE table SET column = 'new_value' WHERE condition;
Data deletion: DELETE FROM table WHERE condition;
Table creation: CREATE TABLE table (column1 data_type, column2 data_type);
Index creation: CREATE INDEX index_name ON table (column);
R:
Dataframe creation: df <- data.frame(column1 = c(1, 2, 3), column2 = c(4, 5, 6))
Data merging: merge(df1, df2, by = 'common_column')
GroupBy: aggregate(df$column, by = list(df$group), FUN = sum)
Data visualization: ggplot(df, aes(x = column)) + geom_bar()
Machine Learning: library(caret); model <- train(column ~ ., data = df)
Regular Expressions (regex):
Match email: \b[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,}\b
Match phone number: \d{3}[-.]?\d{3}[-.]?\d{4}
Match date (YYYY-MM-DD): \d{4}[-.]\d{2}[-.]\d{2}

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