Looping through dataframes

Question

I have 5 dataframes A through E and I'd like to apply the same process to all frames. I've executed a loop but it doesn't overwrite the original dataframes I'd like to change, they result as identical to the frames I've fed to the loop:

frames = [A,B,C,D,E]

for df in frames:
    df = df[df.columns.drop(list(df.filter(regex='Unnamed')))] # Drop columns with "Unnamed" in column name
    df = df.apply(lambda x: x.astype(str).str.upper()) # Convert columns to caps
    df['Unique Name'] = df['Name'] + ' ' + df['Gender'] + ' ' + df['Classification'] + ' ' + df['Silhouette']

The exact same code works if I run it individually for each dataframe.

SAL · Accepted Answer · 2021-02-02 11:10:48Z

1

You can try following:

frames = [A,B,C,D,E]
def update(df):
       df = df[df.columns.drop(list(df.filter(regex='Unnamed')))] # Drop columns with "Unnamed" in column name
       df = df.apply(lambda x: x.astype(str).str.upper()) # Convert columns to caps
       df['Unique Name'] = df['Name'] + ' ' + df['Gender'] + ' ' + df['Classification'] + ' ' + df['Silhouette']
       return df
for i in frames:
  i = update(i)

The issue with your code is you are just overwrite df, instead of updating your actual Dataframes.

answered Feb 2, 2021 at 11:10

SAL

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Comments

Stephan Kulla · Accepted Answer · 2021-02-02 11:09:17Z

0

You can use frames[i] inside the loop instead of df:

for i in range(len(frames)):
  frames[i] = frames[i][frames[i].columns. ...
  ...

Thus you overwrite the data frames inside the list. An easy adaptation of your code:

for i, df in zip(range(len(frames)), frames):
  df = df[df.columns.drop(list(df.filter(regex='Unnamed')))] # Drop columns with "Unnamed" in column name
  df = df.apply(lambda x: x.astype(str).str.upper()) # Convert columns to caps
  df['Unique Name'] = df['Name'] + ' ' + df['Gender'] + ' ' + df['Classification'] + ' ' + df['Silhouette']

  frames[i] = df

answered Feb 2, 2021 at 11:09

Stephan Kulla

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