This is a draft cheat sheet. It is a work in progress and is not finished yet.
Show installed versions
pd.__versions__ |
Python version |
pd.show_versions() |
Dependency & versions |
Create an example DataFrame
df = pd.DataFrame({'col one':[100, 200], 'col two':[300, 400]}) |
Dictionary method |
pd.DataFrame(np.random.rand(4, 8), columns=list('abcdefgh')) |
Rand method |
Rename columns
df = df.rename({'col one':'col_one', 'col two':'col_two'}, axis='columns') |
Overwrite old names (keys) with new names (values) |
df.columns = ['col_one', 'col_two'] |
Rename all of the columns at once |
df.add_prefix('X_') |
Add a prefix |
df.add_suffix('_Y') |
Add a suffix |
Reverse row order
drinks.loc[::-1].head() |
Reverse only |
drinks.loc[::-1].reset_index(drop=True).head() |
Reverse and reset index |
Reverse column order
drinks.loc[:, ::-1].head() |
Reverse the left-to-right order of your columns |
Select columns by data type
drinks.select_dtypes(include='number') |
To select only the numeric columns |
drinks.select_dtypes(include=['number', 'object', 'category', 'datetime']) |
Include multiple data types by passing a list |
drinks.select_dtypes(exclude='number') |
Exclude certain data types |
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Convert strings to numbers
df.astype({'col_one':'float', 'col_two':'float'}).dtypes |
To do mathematical operations on these columns, we need to convert the data types to numeric. This will fail if there are ‘-‘ or NAN |
pd.to_numeric(df.col_three, errors='coerce').fillna(0) |
If you know that the NaN values actually represent zeros, you can fill them with zeros using the fillna() method |
df = df.apply(pd.to_numeric, errors='coerce').fillna(0) df |
you can apply this function to the entire DataFrame all at once by using the apply() method |
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