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3 min readTo divide text after a symbol into rows in pandas, you can use the str.split() function along with the expand=True parameter to create a new DataFrame with the split values in separate rows. For example, if you have a column 'text' in your DataFrame and you want to split the text after a comma ',', you can use the following code: df['text_split'] = df['text'].str.
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5 min readTo use group_concat with having clause in pandas, you can first group your DataFrame by the desired columns using the groupby method. Then, you can use the agg function to apply a custom aggregation function that concatenates the values within each group using the group_concat function. Finally, you can filter the groups based on a condition using the having clause by chaining the filter method after the aggregation.
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4 min readTo get a range of values in the secondary index of a pandas dataframe, you can use the loc accessor along with slicing. For example, if your dataframe has a secondary index called secondary_index and you want to get values in a specific range of this index, you can do so by using:df.loc['value1':'value2', :]This will return the values in the secondary index that fall within the range from value1 to value2.
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3 min readTo filter on a string column using the between clause in pandas, you can use the str.contains() method to check if a string falls within a specified range. First, you would create a boolean mask by using str.contains() with the between() function to specify the range of values you want to filter for in the string column. Then, you can use this boolean mask to filter the DataFrame and retrieve the desired data points.
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3 min readTo split a column in pandas, you can use the str.split() method to split the values in a column based on a specified delimiter. This will create a new column with a list of strings that result from the split. You can then use the expand=True parameter to expand the list of strings into separate columns. Alternatively, you can use the str.extract() method to extract specific patterns from the values in a column and create new columns with the extracted values.
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7 min readTo merge two dataframes based on multiple columns in pandas, you can use the merge() function and pass the column names on which you want to base the merge using the on parameter. For example: merged_df = pd.merge(df1, df2, on=['col1', 'col2']) This will merge df1 and df2 based on the values in columns col1 and col2. If you want to perform a left join, you can use the how parameter: merged_df = pd.
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7 min readTo find values from multiple conditions in pandas, you can use the loc function with boolean indexing. You can create a boolean mask by combining multiple conditions using logical operators such as & (and) or | (or). Then, you can use the loc function to select rows in the DataFrame that meet the specified conditions. By using this method, you can easily filter out the values that meet your criteria from a DataFrame in pandas.
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4 min readTo get specific rows in a CSV file using pandas, you can use the loc method with boolean indexing. First, read the CSV file into a pandas dataframe using the read_csv function. Then, specify the condition that you want to filter on using column values. Finally, use the loc method to subset the dataframe based on the condition. For example, if you want to get rows where the values in the 'column_name' column are greater than 10, you can do this by using df.
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6 min readWhen dealing with headers with merged cells in Excel in Pandas, it can be a bit tricky to handle. The merged cells create a hierarchical structure in the headers, which may cause some complications when importing the data into a Pandas DataFrame.To handle this situation, one approach is to iterate through the headers row by row and create a new header structure that reflects the merged cells. This can be done by using the pd.MultiIndex.
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2 min readTo show all elements of a series using pandas, you can simply print the series itself. Pandas automatically displays all elements in a series when you print it to the console. You can also use the.head() or .tail() methods to display the first or last few elements of a series, respectively. Additionally, you can specify the number of elements to display using the .head(n) or .tail(n) methods, where n is the desired number of elements to show.
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5 min readTo reorder data with pandas, you can use the "reindex" method. This method allows you to change the order of the rows and columns in a DataFrame by specifying a new order for the index and columns. You can also use the "loc" method to select and reorder specific rows and columns based on their labels. Additionally, you can use the "iloc" method to select and reorder rows and columns based on their integer positions.