Data Processing & Cleaning

Anh-Thi Dinh
Data Science
In this note, I use df as DataFrame, s as Series.

Libraries

1import pandas as pd # import pandas package
2import numpy as np

Things need to be checked

  1. csv file:
    1. Values are separated by , of ;?
    2. Encoding.
    3. Timestamp type.
  1. Indexes are sorted?
  1. Indexes are continuous with step 1 (especially after using .dropna() or .drop_duplicates)?
  1. Are there NaN values? Drop them?
  1. Are there duplicates? Drop them?
  1. How many unique values?
  1. For 0/1 features, they have only 2 unique values (0 and 1)?
  1. KDE plot to check the values distribution.
  1. The number of columns?
  1. Unique labels?
  1. Time series:
    1. Time range.
    2. Time step.
    3. Timestamp's type.
    4. Timezone.
    5. Timestamps are monotonic?

Deal with columns

Remove or Keep some

1# REMOVING COLUMNS
2df.drop('New', axis=1, inplace=True) # drop column 'New'
3df.drop(['col1', 'col2'], axis=1, inplace=True)
1# ONLY KEEP SOME
2kept_cols = ['col1', 'col2', ...]
3df = df[kept_cols]
1# ALL EXCEPT SOME
2df[df.columns.difference(['b'])]

Rename columns

1# IMPLICITLY
2df.columns = ['Surname', 'Years', 'Grade', 'Location']
1# EXPLICITLY
2df.rename(columns={'Name': 'Surname', 'Ages': 'Years'}, inplace=True)
1# A SPECIFIC COLUMN
2data.rename(columns={'gdp':'log(gdp)'}, inplace=True)
1# RENAME INDEX COLUMN
2df.index.name = 'new_name'

Make index

1# COLUMN HAS UNIQUE VALUES?
2df['col'].is_unique # True if yes
1# INDEX -> NORMAL COLUMN
2df.reset_index(inplace=True)
1# NORMAL COLUMN -> INDEX
2df.set_index('column')
3df.set_index(['col1', 'col2'])

Drop duplicates

👉 Check section "Duplicates” in the note Data Overview.
1# check duplicates
2df['Student'].duplicated().any()
1# remove duplicates in some columns
2df.drop_duplicates(['col1', 'col2'])
3# use "ignore_index=True" if you wanna reset indexes to 0,1,...,n-1

Couple different columns

1df = df0[['Date', 'Heure', 'tH (°C)']].copy()
2df['timestamp'] = df['Date'] + ' ' + df['Heure']
3
4# if you use without `.copy()`
5# WARNING: A value is trying to be set on a copy of a slice from a DataFrame.

Deal with missing values NaN

👉 Check section "Missing values” in the note Data Overview.

Drop NaN values

Full reference of dropna is here.
1# Drop any rows which have any nans
2df.dropna()
1# Drop if all values in that col are NA
2df.dropna(how='all', axis=1)
1# Drop columns that have any nans
2df.dropna(axis=1)
1# Only drop columns having min 90% non-NaNs
2df.dropna(thresh=int(df.shape[0]*.9), axis=1)
1# Only keep rows having >=2 non-NA values
2df.dropna(thresh=2)
1# Only consider some cols
2df.dropna(subset=['col1', 'col2']
1# multi-index
2df.dropna(subset=[(1,'a'), (1,'b'), (2,'a'), (2,'b')])
3
4# consider all cols '1' and '2'
5df.dropna(subset=df.loc[[], [1,2]].columns)
6

Fill NaN with others

Check other methods of fillna here.
1# Fill NaN with ' '
2df['col'] = df['col'].fillna(' ')
1# Fill NaN with 99
2df['col'] = df['col'].fillna(99
1# mean / median of each column
2df.fillna(df.mean())
1# Fill NaN with the mean of the column
2df['col'] = df['col'].fillna(df['col'].mean())
1# Fill NA with mean of row
2m = df.mean(axis=1)
3for col in df.columns:
4    df.loc[:, col] = df.loc[:, col].fillna(m)

Do with conditions

1np.where(if_this_condition_is_true, do_this, else_this)
2df['new_column'] = np.where(df[i] > 10, 'foo', 'bar) # example

Work with text data

There are a lot of methods we can work with text data (pd.Series.str). We can use it coupling with regular expression.
 
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