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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.
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3 min readTo extract a table from multiple Excel documents and import it into pandas, you can use the pandas library and the read_excel function. First, you need to loop through each Excel file and read the specific sheet containing the table data using the read_excel function. Next, you can append the data from each file into a pandas DataFrame. This can be achieved by creating an empty list to store the DataFrames and then concatenating them into a single DataFrame using the pd.concat function.
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6 min readTo unwind a column in a pandas dataframe, you can use the explode() function. This function will take a column with lists as values and create new rows for each element in the list. This is useful when you have a column with nested values that you want to separate out into individual rows. By using the explode() function, you can effectively unwind a column in a pandas dataframe and create a more structured and accessible dataset for further analysis or manipulation.
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4 min readTo remove empty strings in a pandas DataFrame, you can use the replace() method in combination with the np.nan function from the NumPy library. First, import the NumPy library by using import numpy as np. Then, you can replace empty strings with np.nan by applying the following code snippet: df.replace('', np.nan, inplace=True). This will replace all empty strings in the DataFrame named df with NaN values.
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4 min readTo iterate over specific indices in a pandas DataFrame, you can use the iloc function. This function allows you to access rows and columns by their integer index position.For example, if you want to iterate over specific rows in a DataFrame based on their index positions, you can use a for loop with the iloc function like this: import pandas as pd data = {'A':[1, 2, 3, 4, 5], 'B':[10, 20, 30, 40, 50], 'C':[100, 200, 300, 400, 500]} df = pd.
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4 min readTo merge two pandas series, you can use the pd.concat() function. This function allows you to concatenate two series along a specified axis. By default, the function concatenates the series along the rows (axis=0), but you can also concatenate them along the columns (axis=1) if needed.Here's an example of how to merge two pandas series: import pandas as pd # Create two pandas series series1 = pd.Series([1, 2, 3]) series2 = pd.
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4 min readTo edit a CSV file using pandas in Python, you first need to import the pandas library. Then you can read the CSV file into a pandas DataFrame using the read_csv function. Once you have the data in a DataFrame, you can manipulate the data by selecting specific rows or columns, filtering the data, or updating values. Finally, you can save the edited DataFrame back to a CSV file using the to_csv function.[rating:b1c44d88-9206-437e-9aff-ba3e2c424e8f]How to append data to a CSV file using pandas.
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7 min readThere are multiple ways to check if a script was successful in PowerShell. One common method is to use the automatic variable "$?" which returns a boolean value indicating the success or failure of the last executed command. Another way is to set a specific exit code in the script using the "exit" keyword and checking for that exit code after running the script.