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Diffstat (limited to 'tutorials/module_4/4.3 Importing and Managing Data.md')
| -rw-r--r-- | tutorials/module_4/4.3 Importing and Managing Data.md | 11 |
1 files changed, 9 insertions, 2 deletions
diff --git a/tutorials/module_4/4.3 Importing and Managing Data.md b/tutorials/module_4/4.3 Importing and Managing Data.md index cd66164..91411c6 100644 --- a/tutorials/module_4/4.3 Importing and Managing Data.md +++ b/tutorials/module_4/4.3 Importing and Managing Data.md @@ -86,11 +86,17 @@ df.to_csv("edited_experiment.csv", index=False) This workflow makes pandas ideal for working with tabular data, you can quickly edit or generate datasets, verify values, and save clean, structured files for later visualization or analysis. -### Problem 2: Import time stamped data +### Problem: Import time stamped data -### Further Docs + + + + + + +# Further Docs [Comparison with Spreadsheets](https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_spreadsheets.html#compare-with-spreadsheets) [Intro to Reading/Writing Files](https://pandas.pydata.org/docs/getting_started/intro_tutorials/02_read_write.html) [Subsetting Data](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html) @@ -98,3 +104,4 @@ This workflow makes pandas ideal for working with tabular data, you can quickly [Reshaping Data](https://pandas.pydata.org/docs/user_guide/reshaping.html) [Merging DataFrames](https://pandas.pydata.org/docs/user_guide/merging.html) [Combining DataFrames](https://pandas.pydata.org/docs/getting_started/intro_tutorials/08_combine_dataframes.html) + |