In this note, I use 
df as DataFrame, s as Series.- csvfile:
- Values are separated by ,of;?
- Encoding.
- Timestamp type.
- Indexes are sorted?
- Indexes are continuous with step 1 (especially after using .dropna()or.drop_duplicates)?
- Are there NaNvalues? Drop them?
- Are there duplicates? Drop them?
- How many unique values?
- For 0/1features, they have only 2 unique values (0and1)?
- KDEplot to check the values distribution.
- The number of columns?
- Unique labels?
- Time series:
- Time range.
- Time step.
- Timestamp's type.
- Timezone.
- Timestamps are monotonic?
👉 Check section "Duplicates” in the note Data Overview.
👉 Check section "Missing values” in the note Data Overview.
Full reference of 
dropna is here.Check other methods of 
fillna here.There are a lot of methods we can work with text data (
pd.Series.str). We can use it coupling with regular expression.