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This is not too bad. It's a good thing you use keyword arguments for the replace method I always try to keep my original data in its original state, and continue with the cleaned dataframe. fluent sty …

Try to get a good book or tutorial about pandas. …

groupby When iterating over the values in a column, it is bad practice to hardcode the values (for pivot in [1, 2, 3]). A better way would have been for pivot in df["dof"].unique(), but the best way …

You will not get dramatic speedups untill you can vectorize the operations, but here are some tips already indexing before iterating instead of for i, row in df.iterrows(): if row['event'] == …

Review all in all, this code is rather clean. I would use a generator comprehension and itertools.chain in fake_disrete_data instead of the nested for-loop, but that is a matter of taste linewraps …

= cluster] if changed_indices.empty: clusters[indices] = indices.min() else: clusters[indices] = changed_indices.min() This makes use of the pandas indexing. … This method can also be easily tested in isolation groupby Instead of iterating over the unique values, pandas has its groupby functionality. …

np.random.randint(1000, 2000, size=size), date_label: pd.DatetimeIndex(start=start, freq=freq, periods=size), } ) summarize The rolling mean and std you do can be done with builtin pandas …

= np.array( [np.roll(arr, -i) for i in range(l)], copy=False, ) / arr - mask return pd.DataFrame(data = result.T, index = data.index) I find this code less clear than the pandas …

leave the original data intact Since df is the original data, adding columns to it can have a strange side effect in another part of the calculation. Best is not to touch the original data, and do al …

drink: { continent: data.loc[ data[drink].nlargest(5).index, ["country", drink] ] for continent, data in data_by_continent.items() } for drink in drinks } pandas …

Here you can use pandas further. For starters, you can make the year a pandas.Period, so you can index the year immediately. Then you can do the division by 100 already. … Since pandas has a nice cumprod function, you can already add 1 to each index def get_inflation_rate( filename: typing.Union[Path, str, typing.IO] ) -> pd.Series: """Read the inflation data from …

you can use groupby.transform df["next_timestamp"] = df.groupby("id")["time_stamp"].transform( lambda x: x.shift(-1) )

self Why put this method on a class? the lack of use of self in the method should act as a flag string result you format the mismatch as a string (f"df1:{val}, df2: {df2.dtypes[key]}"). This way yo …

list) for i in df2.index: nameid[df2.loc[i, 'Name']].append(df2.loc[i, 'People ID']) would work iteration apart from the fact that you want to prevent iteration as much as possible when using pandas … namead = { name for name, ids in nameid.items() if len(ids) > 1 } pandas indexing dupes = [i for i in df2.index if df2.loc[i, 'Name'] in namead.keys()] for i in duperevs: df2.loc[i, …

iterrows Of all the ways to iterate over a pandas DataFrame, iterrows is the worst. …