Posts - Page 67 (page 67)
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5 min readTo replace .append with .concat in pandas dataframe, you can use the pd.concat() function instead. This function allows you to concatenate two or more dataframes along a particular axis. Simply pass in the dataframes you want to concatenate as arguments to pd.concat() and specify the axis along which you want to concatenate them. This replaces the need for using the .append() method on individual dataframes.[rating:b1c44d88-9206-437e-9aff-ba3e2c424e8f]How to replace .append with .
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4 min readTo turn a column header into a pandas index, you can use the set_index() method in pandas. This method allows you to specify which column you want to set as the index for your DataFrame. By passing the name of the column as an argument to set_index(), you can make that column the new index for your DataFrame. This will convert the column header into the index for your data.
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4 min readTo group by data in a column with pandas, you can use the groupby() function along with the column you want to group by. This function allows you to split the data into groups based on a particular column, and then perform operations on these groups. You can then apply various aggregation functions to calculate statistics for each group, such as mean, count, sum, etc.
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4 min readTo get a specific column from a list into a pandas dataframe, you can create a dictionary from the list and then convert it into a dataframe. First, create a dictionary with the column name as the key and the corresponding values from the list as the values. Next, convert the dictionary into a pandas dataframe using the pd.DataFrame() function. Finally, you can access the specific column by using the column name as an index.
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4 min readTo read Excel line by line in Pandas, you can use the read_excel() function along with setting appropriate parameters. By default, read_excel() reads the entire Excel file into a DataFrame, but you can use the chunksize parameter to specify the number of rows to read at a time. This allows you to read the Excel file line by line or in chunks. You can then iterate over the chunks to process one line at a time.
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3 min readPandas allows you to select specific columns from a DataFrame using the column names. You can use square brackets [] with the column name inside to select a single column, or you can pass a list of column names to select multiple columns. Additionally, you can use a range of columns by specifying the start and end columns in the list. You can also use the loc and iloc methods to select columns by their label or index, respectively.
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6 min readTo filter a CSV file using pandas by multiple values, you can use the following code snippet:df = pd.read_csv('file.csv')filtered_df = df[df['column_name'].isin(['value1', 'value2', 'value3'])]This code reads the CSV file into a pandas DataFrame, and then filters the DataFrame to include only rows where the column 'column_name' matches one of the specified values (value1, value2, or value3).
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5 min readYou can add a counter to duplicated index values in a pandas DataFrame by using the groupby() and cumcount() functions.First, you need to reset the index of the DataFrame using the reset_index() function so that the duplicated index values become a normal column. Then, you can use the groupby() function with the index column as the key to group the rows with duplicated index values together.
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3 min readTo read a JSON data into a dataframe using pandas, you can use the pd.read_json() function provided by the pandas library. This function can take a JSON string or file path as input and convert it into a pandas dataframe.You can simply pass the JSON data as a string or specify the file path to the JSON file that you want to read. The pd.read_json() function will automatically parse the JSON data and create a dataframe with the appropriate column names and values.
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3 min readTo sum up values from a Pandas DataFrame column, you can use the sum() function along with the desired column name. For example, if you have a DataFrame named df and you want to calculate the sum of values in a column named column_name, you can use df['column_name'].sum(). This will return the sum of all the values in that specific column.[rating:b1c44d88-9206-437e-9aff-ba3e2c424e8f]How to sort the sum of values in a pandas dataframe column in descending order.
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5 min readTo create multiple columns in a pandas DataFrame, you can simply pass a Python dictionary where the keys are the column names and the values are the data you want to populate in those columns. For example, you can create a DataFrame with three columns named 'A', 'B', and 'C' by passing a dictionary like this: import pandas as pd data = {'A': [1, 2, 3], 'B': ['foo', 'bar', 'baz'], 'C': [True, False, True]} df = pd.
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6 min readTo rearrange nested pandas dataframe columns, you can simply use the reindex function with a list of the desired column names in the order you want them to appear. This will create a new dataframe with the columns rearranged as per your specifications. Additionally, you can also use indexing and slicing operations to achieve the same results. Make sure to check the documentation for more information on manipulating pandas dataframes effectively.