Скачать или смотреть Combine multiple csv files using python

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combining multiple csv files in python can be accomplished easily using the `pandas` library, which provides powerful tools for data manipulation and analysis. below is a step-by-step tutorial on how to combine multiple csv files into a single dataframe.

step 1: install pandas

if you haven't already installed pandas, you can do so using pip. run the following command in your terminal:



step 2: prepare your csv files

ensure that all the csv files you want to combine are in the same directory and have the same structure (i.e., same columns). for this example, let's say we have three csv files (`data1.csv`, `data2.csv`, and `data3.csv`) with the following content:

*data1.csv*


*data2.csv*


*data3.csv*


step 3: write python code to combine csv files

here's a simple python script that uses `pandas` to combine the csv files:



explanation of the code

1. **import libraries**: import the `pandas` library and `glob` for file handling.

2. **file path**: specify the path where your csv files are stored.

3. **get csv files**: use `glob.glob()` to get a list of all csv files in the specified directory.

4. **read and store dataframes**: loop over the list of files, read each csv file into a dataframe using `pd.read_csv()`, and append each dataframe to a list.

5. **concatenate dataframes**: use `pd.concat()` to combine all dataframes in the list into a single dataframe. the `ignore_index=true` argument resets the index in the combined dataframe.

6. **display the result**: print the combined dataframe to the console.

7. **save combined dataframe**: optionally, save the combined dataframe to a new csv file using `to_csv()`.

step 4: run the code

replace the `path` variable with the actual directory path where your csv files are located, and run the script. after successful execution, you should see the combined dataframe printed in the console, and a new csv file named `combined_data.csv` will be created in your working directory.

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