PandasII: Exploration and Manipulation#
+PandasII: Exploration#
What you will learn:
-
-
Introduce pandas dataframes and the essential operations
+Use Pandas for data inspection and exploration
+Selection, indexing and slicing in Pandas
+Sorting and ranking
+Data manipulation
# import dependencies
@@ -408,7 +412,7 @@ PandasII: Exploration and Manipulation
iris_df = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/refs/heads/master/iris.csv")
@@ -536,7 +540,7 @@ PandasII: Exploration and Manipulationiris:
+Check that we have a dataframe:
-shape: As with NumPy, the shape of the dataframe (rows, columns).
shape: As with NumPy, the shape of the dataframe (number of rows, number of columns).
-info():
info(): prints information about the dataframe including the index dtype and columns, non-null values and memory usage.
@@ -1336,7 +1340,7 @@ Summarizing data
-/tmp/ipykernel_105955/1934569051.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
+/tmp/ipykernel_15133/1934569051.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
iris_df.corr()
@@ -1497,34 +1501,68 @@ Summarizing data
-Selection and Indexing#
-
-By Index#
-We use iloc[] to extract rows using indexes.
+
+Working with columns#
+
+Selection#
+
+bracket notation: variable name must be a string
-# This fetches row 3, and all columns:
-
-iris_df.iloc[2]
+iris_df['sepal_length'], type(iris_df['sepal_length'])
-sepal_length 4.7
-sepal_width 3.2
-petal_length 1.3
-petal_width 0.2
-species setosa
-Name: 2, dtype: object
+(0 5.1
+ 1 4.9
+ 2 4.7
+ 3 4.6
+ 4 5.0
+ ...
+ 145 6.7
+ 146 6.3
+ 147 6.5
+ 148 6.2
+ 149 5.9
+ Name: sepal_length, Length: 150, dtype: float64,
+ pandas.core.series.Series)
-fetch rows with indices 1,2 (the right endpoint is exclusive), and all columns.
-iris_df.iloc[1:3]
+# double bracket gives you the selected column as new dataframe
+iris_df[['sepal_length']], type(iris_df[['sepal_length']])
+
+
+
+
+( sepal_length
+ 0 5.1
+ 1 4.9
+ 2 4.7
+ 3 4.6
+ 4 5.0
+ .. ...
+ 145 6.7
+ 146 6.3
+ 147 6.5
+ 148 6.2
+ 149 5.9
+
+ [150 rows x 1 columns],
+ pandas.core.frame.DataFrame)
+
+
+
+
+This notation allows you to select more than one column
+
+
+iris_df[['sepal_length', 'petal_length']]
@@ -1548,37 +1586,109 @@ By Index
sepal_length
- sepal_width
petal_length
- petal_width
- species
+
+ 0
+ 5.1
+ 1.4
+
1
4.9
- 3.0
1.4
- 0.2
- setosa
2
4.7
- 3.2
1.3
- 0.2
- setosa
+
+
+ 3
+ 4.6
+ 1.5
+
+
+ 4
+ 5.0
+ 1.4
+
+
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 5.2
+
+
+ 146
+ 6.3
+ 5.0
+
+
+ 147
+ 6.5
+ 5.2
+
+
+ 148
+ 6.2
+ 5.4
+
+
+ 149
+ 5.9
+ 5.1
+150 rows × 2 columns
-fetch rows with indices 1,2 and first three columns (positions 0, 1, 2)
+
+Dot notation: Here columns are object attributes
-iris_df.iloc[1:3, 0:3]
+iris_df.sepal_length, type(iris_df.sepal_length)
+
+
+
+
+(0 5.1
+ 1 4.9
+ 2 4.7
+ 3 4.6
+ 4 5.0
+ ...
+ 145 6.7
+ 146 6.3
+ 147 6.5
+ 148 6.2
+ 149 5.9
+ Name: sepal_length, Length: 150, dtype: float64,
+ pandas.core.series.Series)
+
+
+
+
+Dot notation is very convenient, since as object attributes they can be tab-completed in various editing environments.
+But:
+
+It only works if the column names are not reserved words
It can’t be used when created a new column (see below)
It allows you to select just one column
+
+
+iris_df['sepal_volume'] = iris_df.sepal_length * iris_df.sepal_width
+
+iris_df.head()
@@ -1604,48 +1714,73 @@ By Index
sepal_length
sepal_width
petal_length
+ petal_width
+ species
+ sepal_volume
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+
1
4.9
3.0
1.4
+ 0.2
+ setosa
+ 14.70
2
4.7
3.2
1.3
+ 0.2
+ setosa
+ 15.04
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+ 18.00
-You can apply slices to column names too. You don’t need .iloc[] here.
-
-
-iris_df.columns[0:3]
-
-
-
-
-Index(['sepal_length', 'sepal_width', 'petal_length'], dtype='object')
-
-
-
-
-
-
-By label#
-We can select by row and column labels using .loc[].
-Here we ask for rows with labels (indexes) 1-3, and it gives exactly that
-.iloc[] returned rows with indices 1,2.
-Author note: This is by far the more useful of the two in my experience.
+Note that:
+
+The left side has form: DataFrame name, bracket notation, new column name
The assignment operator = is used
The right side contains an expression; here, two df columns are multiplied together
+Bracket notation also works on the fields, but it’s more typing:
-iris_df.loc[1:3]
+iris_df['sepal_volume_2'] = iris_df['sepal_length'] * iris_df['sepal_width']
+
+iris_df.head()
@@ -1673,9 +1808,21 @@ By label
petal_length
petal_width
species
+ sepal_volume
+ sepal_volume_2
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+ 17.85
+
1
4.9
@@ -1683,6 +1830,8 @@ By label
1.4
0.2
setosa
+ 14.70
+ 14.70
2
@@ -1691,6 +1840,8 @@ By label
1.3
0.2
setosa
+ 15.04
+ 15.04
3
@@ -1699,15 +1850,68 @@ By label
1.5
0.2
setosa
+ 14.26
+ 14.26
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+ 18.00
+ 18.00
-Subset on columns with column name (as a string) or list of strings
+The bracket notation must be used when assigning to a new column. This will break:
-iris_df.loc[1:3, ['sepal_length','petal_width']]
+iris_df.'sepal_volume_3' = iris_df.sepal_length + iris_df.sepal_width
+
+
+
+
+ Cell In[28], line 1
+ iris_df.'sepal_volume_3' = iris_df.sepal_length + iris_df.sepal_width
+ ^
+SyntaxError: invalid syntax
+
+
+
+
+
+
+iris_df.sepal_volume_3 = iris_df.sepal_length + iris_df.sepal_width
+
+
+
+
+/tmp/ipykernel_26349/2179810851.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+ iris_df.sepal_volume_3 = iris_df.sepal_length + iris_df.sepal_width
+
+
+
+
+
+
+Removing#
+
+Using the reserverd keyword del to drop a DataFrame or single columns from the dataframe:
+
+
+iris_df_drop = iris_df.copy()
+
+
+
+
+
-Select all rows, specific columns
-iris_df.loc[:, ['sepal_length','petal_width']]
+# delete the column 'x'
+del iris_df_drop['sepal_volume_2']
+
+
+
+
+
-
-
-Boolean Filtering#
-It’s very common to subset a dataframe based on some condition on the data.
-🔑 Note that even though we are filtering rows, we are not using .loc[] or .iloc[] here.
-Pandas knows what to do if you pass a boolean structure.
-
-
-iris_df.sepal_length >= 7.5
-
-
-
-
-0 False
-1 False
-2 False
-3 False
-4 False
- ...
-145 False
-146 False
-147 False
-148 False
-149 False
-Name: sepal_length, Length: 150, dtype: bool
-
-
-
-
+
+Using the method drop() to drop one or more columns by specifying axis argument equal 1:
-iris_df.loc[iris_df.sepal_length >= 7.5,:]
+# Here we drop columns
+iris_df_drop = iris_df_drop.drop(['sepal_volume', 'species'], axis=1)
+iris_df_drop
@@ -1903,65 +2148,97 @@ Boolean Filteringdrop() method you can also drop specific observations by setting axis=0
-iris_df.loc[(iris_df['sepal_length' ]>= 4.5) & (iris_df['sepal_length'] <= 4.7),:]
+# Now a particular observation
+iris_df_drop = iris_df_drop.drop([0], axis=0)
+iris_df_drop
@@ -1988,17 +2265,22 @@ Boolean Filtering
-Masking#
-Here’s an example of masking using boolean conditions passed to the dataframe selector:
-Here are the values for the feature sepal length:
+
+Working with the dataframe as a whole#
+
+iloc[]: Selection by index#
+We can use iloc[] to extract rows and columns using indexes.
-iris_df.sepal_length.values
+# This fetches row 3, and all columns:
+iris_df.iloc[2]
-array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,
- 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,
- 5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,
- 5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. , 7. , 6.4,
- 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5. , 5.9, 6. , 6.1, 5.6,
- 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7,
- 6. , 5.7, 5.5, 5.5, 5.8, 6. , 5.4, 6. , 6.7, 6.3, 5.6, 5.5, 5.5,
- 6.1, 5.8, 5. , 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3,
- 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5,
- 7.7, 7.7, 6. , 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2,
- 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6. , 6.9, 6.7, 6.9, 5.8,
- 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9])
+sepal_length 4.7
+sepal_width 3.2
+petal_length 1.3
+petal_width 0.2
+species setosa
+sepal_volume 15.04
+sepal_volume_2 15.04
+Name: 2, dtype: object
-And here are the boolean values generated by applying a comparison operator to those values:
-mask = iris_df.sepal_length >= 7.5
-
-
-
-
-
-
-mask.values
-
-
-
-
-array([False, False, False, False, False, False, False, False, False,
- False, False, False, False, False, False, False, False, False,
- False, False, False, False, False, False, False, False, False,
- False, False, False, False, False, False, False, False, False,
- False, False, False, False, False, False, False, False, False,
- False, False, False, False, False, False, False, False, False,
- False, False, False, False, False, False, False, False, False,
- False, False, False, False, False, False, False, False, False,
- False, False, False, False, False, False, False, False, False,
- False, False, False, False, False, False, False, False, False,
- False, False, False, False, False, False, False, False, False,
- False, False, False, False, False, False, True, False, False,
- False, False, False, False, False, False, False, False, False,
- True, True, False, False, False, True, False, False, False,
- False, False, False, False, False, True, False, False, False,
- True, False, False, False, False, False, False, False, False,
- False, False, False, False, False, False])
-
-
-
-
-The two sets of values have the same shape.
-We can now overlay the logical values over the numeric ones and keep only what is True:
-
-
-iris_df.sepal_length[mask].values
+# Similar to
+iris_df.iloc[2, :]
-array([7.6, 7.7, 7.7, 7.7, 7.9, 7.7])
+sepal_length 4.7
+sepal_width 3.2
+petal_length 1.3
+petal_width 0.2
+species setosa
+sepal_volume 15.04
+sepal_volume_2 15.04
+Name: 2, dtype: object
-
-
-Sorting and Ranking#
-.sort_values()
-Sort by values
-
-by parameter takes string or list of strings
ascending takes True or False
inplace will save sorted values into the df
-
+fetch rows with indices 1,2 (the right endpoint is exclusive), and all columns.
-iris_df.sort_values(by=['sepal_length','petal_width'])
+iris_df.iloc[1:3]
@@ -2172,109 +2428,39 @@ Sorting and Ranking
-.sort_index()#
-Sort by index. Example sorts by descending index
+fetch rows with indices 1,2 and first three columns (positions 0, 1, 2)
-iris_df.sort_index(axis=0, ascending=False)
+iris_df.iloc[1:3, 0:3]
@@ -2300,128 +2486,48 @@ .sort_index()sepal_length
sepal_width
petal_length
- petal_width
- species
- 149
- 5.9
- 3.0
- 5.1
- 1.8
- virginica
-
-
- 148
- 6.2
- 3.4
- 5.4
- 2.3
- virginica
-
-
- 147
- 6.5
- 3.0
- 5.2
- 2.0
- virginica
-
-
- 146
- 6.3
- 2.5
- 5.0
- 1.9
- virginica
-
-
- 145
- 6.7
+ 1
+ 4.9
3.0
- 5.2
- 2.3
- virginica
-
-
- ...
- ...
- ...
- ...
- ...
- ...
-
-
- 4
- 5.0
- 3.6
1.4
- 0.2
- setosa
-
-
- 3
- 4.6
- 3.1
- 1.5
- 0.2
- setosa
2
4.7
3.2
1.3
- 0.2
- setosa
-
-
- 1
- 4.9
- 3.0
- 1.4
- 0.2
- setosa
-
-
- 0
- 5.1
- 3.5
- 1.4
- 0.2
- setosa
-150 rows × 5 columns
-
-
-Dealing with Missing Data#
-Pandas primarily uses the data type np.nan from NumPy to represent missing data.
+You can apply slices to column names too. You don’t need .iloc[] here.
-import numpy as np
+iris_df.columns[0:3]
-
-
-
-df_miss = pd.DataFrame({
- 'x':[2, np.nan, 1],
- 'y':[np.nan, np.nan, 6]}
-)
+
+Index(['sepal_length', 'sepal_width', 'petal_length'], dtype='object')
+
+
+loc[]: Selection by label#
+We can select by row and column labels using .loc[].
+Here we ask for rows with labels (indexes) 1-3, and it gives exactly that
+.iloc[] returned rows with indices 1,2.
+Author note: This is by far the more useful of the two in my experience.
-df_miss
+iris_df.loc[1:3]
@@ -2444,39 +2550,54 @@ Dealing with Missing Data
x
- y
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
- 0
- 2.0
- NaN
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+ 14.70
+ 14.70
- 1
- NaN
- NaN
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 15.04
+ 15.04
- 2
- 1.0
- 6.0
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+ 14.26
-
-
-.dropna()#
-This will drop all rows with missing data in any column.
-
+Subset on columns with column name (as a string) or list of strings
-df_drop_all = df_miss.dropna()
-df_drop_all
+iris_df.loc[1:3, ['sepal_length','petal_width']]
@@ -2499,25 +2620,34 @@ .dropna()
x
- y
+ sepal_length
+ petal_width
+
+ 1
+ 4.9
+ 0.2
+
2
- 1.0
- 6.0
+ 4.7
+ 0.2
+
+
+ 3
+ 4.6
+ 0.2
-The subset parameter takes a list of column names to specify which columns should have missing values.
+Select all rows, specific columns
-df_drop_x = df_miss.dropna(subset=['x'])
-df_drop_x
+iris_df.loc[:, ['sepal_length','petal_width']]
@@ -2540,41 +2670,102 @@ .dropna()
x
- y
+ sepal_length
+ petal_width
0
- 2.0
- NaN
+ 5.1
+ 0.2
+
+
+ 1
+ 4.9
+ 0.2
2
- 1.0
- 6.0
+ 4.7
+ 0.2
+
+
+ 3
+ 4.6
+ 0.2
+
+
+ 4
+ 5.0
+ 0.2
+
+
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 2.3
+
+
+ 146
+ 6.3
+ 1.9
+
+
+ 147
+ 6.5
+ 2.0
+
+
+ 148
+ 6.2
+ 2.3
+
+
+ 149
+ 5.9
+ 1.8
+150 rows × 2 columns
-
-.fillna()#
-This will replace missing values with whatever you set it to, e.g. \(0\)s.
-
-We can pass the results of an operation – for example to peform simple imputation, we can replace missing values in each column with the median value of the respective column:
+
+Boolean Filtering#
+It’s very common to subset a dataframe based on some condition on the data.
+Pandas knows what to do if you pass a boolean structure.
-df_filled = df_miss.fillna(df_miss.median())
+iris_df.sepal_length >= 7.5
+
+
+
+
+0 False
+1 False
+2 False
+3 False
+4 False
+ ...
+145 False
+146 False
+147 False
+148 False
+149 False
+Name: sepal_length, Length: 150, dtype: bool
-df_filled
+iris_df.loc[iris_df.sepal_length >= 7.5,:]
@@ -2597,165 +2788,284 @@ .fillna()
x
- y
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
- 0
+ 105
+ 7.6
+ 3.0
+ 6.6
+ 2.1
+ virginica
+ 22.80
+ 22.80
+
+
+ 117
+ 7.7
+ 3.8
+ 6.7
+ 2.2
+ virginica
+ 29.26
+ 29.26
+
+
+ 118
+ 7.7
+ 2.6
+ 6.9
+ 2.3
+ virginica
+ 20.02
+ 20.02
+
+
+ 122
+ 7.7
+ 2.8
+ 6.7
2.0
- 6.0
+ virginica
+ 21.56
+ 21.56
- 1
- 1.5
- 6.0
+ 131
+ 7.9
+ 3.8
+ 6.4
+ 2.0
+ virginica
+ 30.02
+ 30.02
- 2
- 1.0
- 6.0
+ 135
+ 7.7
+ 3.0
+ 6.1
+ 2.3
+ virginica
+ 23.10
+ 23.10
-
-
-Column selection, addition, deletion#
-
-Selection#
-Use bracket notation or dot notation.
-
-bracket notation: variable name must be a string
-df['y'], type(df['y'])
+iris_df.loc[(iris_df['sepal_length' ]>= 4.5) & (iris_df['sepal_length'] <= 4.7),:]
----------------------------------------------------------------------------
-NameError Traceback (most recent call last)
-Cell In[45], line 1
-----> 1 df['y'], type(df['y'])
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
+
+
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 15.04
+ 15.04
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+ 14.26
+
+
+ 6
+ 4.6
+ 3.4
+ 1.4
+ 0.3
+ setosa
+ 15.64
+ 15.64
+
+
+ 22
+ 4.6
+ 3.6
+ 1.0
+ 0.2
+ setosa
+ 16.56
+ 16.56
+
+
+ 29
+ 4.7
+ 3.2
+ 1.6
+ 0.2
+ setosa
+ 15.04
+ 15.04
+
+
+ 41
+ 4.5
+ 2.3
+ 1.3
+ 0.3
+ setosa
+ 10.35
+ 10.35
+
+
+ 47
+ 4.6
+ 3.2
+ 1.4
+ 0.2
+ setosa
+ 14.72
+ 14.72
+
+
+
+
+
+
+
+
+Masking#
+Here’s an example of masking using boolean conditions passed to the dataframe selector:
+Here are the values for the feature sepal length:
+
+
+iris_df.sepal_length.values
+
+array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,
+ 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,
+ 5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,
+ 5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. , 7. , 6.4,
+ 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5. , 5.9, 6. , 6.1, 5.6,
+ 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7,
+ 6. , 5.7, 5.5, 5.5, 5.8, 6. , 5.4, 6. , 6.7, 6.3, 5.6, 5.5, 5.5,
+ 6.1, 5.8, 5. , 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3,
+ 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5,
+ 7.7, 7.7, 6. , 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2,
+ 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6. , 6.9, 6.7, 6.9, 5.8,
+ 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9])
+
+
+
+And here are the boolean values generated by applying a comparison operator to those values:
-# double bracket gives you the selected column as new dataframe
-df[['x']], type(df[['x']])
+mask = iris_df.sepal_length >= 7.5
+
+
+
+
+
+
+mask.values
-( x
- 0 0
- 1 2
- obs3 1
- 3 5,
- pandas.core.frame.DataFrame)
+array([False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, True, False, False,
+ False, False, False, False, False, False, False, False, False,
+ True, True, False, False, False, True, False, False, False,
+ False, False, False, False, False, True, False, False, False,
+ True, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False])
+The two sets of values have the same shape.
+We can now overlay the logical values over the numeric ones and keep only what is True:
-df[['y', 'x']]
+iris_df.sepal_length[mask].values
+
+
+
+
+array([7.6, 7.7, 7.7, 7.7, 7.9, 7.7])
+
+
+
+
+
+
+Sorting#
+
+sort_values(): Sorts dataframe by values. You can customize this sorting with the following parameters:
+
+by parameter takes string or list of strings
ascending takes True or False
inplace will save sorted values into the df
+
+
+
+
+iris_df.sort_values(by=['sepal_length','petal_width'])
@@ -2778,50 +3088,444 @@ Column Selection
y
- x
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
+
+
+
+
+ 13
+ 4.3
+ 3.0
+ 1.1
+ 0.1
+ setosa
+ 12.90
+ 12.90
+
+
+ 8
+ 4.4
+ 2.9
+ 1.4
+ 0.2
+ setosa
+ 12.76
+ 12.76
+
+
+ 38
+ 4.4
+ 3.0
+ 1.3
+ 0.2
+ setosa
+ 13.20
+ 13.20
+
+
+ 42
+ 4.4
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 14.08
+ 14.08
+
+
+ 41
+ 4.5
+ 2.3
+ 1.3
+ 0.3
+ setosa
+ 10.35
+ 10.35
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 122
+ 7.7
+ 2.8
+ 6.7
+ 2.0
+ virginica
+ 21.56
+ 21.56
+
+
+ 117
+ 7.7
+ 3.8
+ 6.7
+ 2.2
+ virginica
+ 29.26
+ 29.26
+
+
+ 118
+ 7.7
+ 2.6
+ 6.9
+ 2.3
+ virginica
+ 20.02
+ 20.02
+
+
+ 135
+ 7.7
+ 3.0
+ 6.1
+ 2.3
+ virginica
+ 23.10
+ 23.10
+
+
+ 131
+ 7.9
+ 3.8
+ 6.4
+ 2.0
+ virginica
+ 30.02
+ 30.02
+
+
+
+
150 rows × 7 columns
+
+
+
+
+iris_df.sort_values(by=['sepal_length','petal_width'], ascending=False)
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
+
+
+
+
+ 131
+ 7.9
+ 3.8
+ 6.4
+ 2.0
+ virginica
+ 30.02
+ 30.02
+
+
+ 118
+ 7.7
+ 2.6
+ 6.9
+ 2.3
+ virginica
+ 20.02
+ 20.02
+
+
+ 135
+ 7.7
+ 3.0
+ 6.1
+ 2.3
+ virginica
+ 23.10
+ 23.10
+
+
+ 117
+ 7.7
+ 3.8
+ 6.7
+ 2.2
+ virginica
+ 29.26
+ 29.26
+
+
+ 122
+ 7.7
+ 2.8
+ 6.7
+ 2.0
+ virginica
+ 21.56
+ 21.56
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 41
+ 4.5
+ 2.3
+ 1.3
+ 0.3
+ setosa
+ 10.35
+ 10.35
+
+
+ 8
+ 4.4
+ 2.9
+ 1.4
+ 0.2
+ setosa
+ 12.76
+ 12.76
+
+
+ 38
+ 4.4
+ 3.0
+ 1.3
+ 0.2
+ setosa
+ 13.20
+ 13.20
+
+
+ 42
+ 4.4
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 14.08
+ 14.08
+
+
+ 13
+ 4.3
+ 3.0
+ 1.1
+ 0.1
+ setosa
+ 12.90
+ 12.90
+
+
+
+150 rows × 7 columns
+
+
+
+sort_index(): Sorts dataframe by index. You can customize this sorting with the following parameters:
+
+axis along which to sort. The value 0 identifies the rows, and 1 identifies the columns.
ascending takes True or False
inplace will save sorted values into the df
+
+
+
+
+iris_df.sort_index(axis=0)
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
0
- 1
- 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+ 17.85
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+ 14.70
+ 14.70
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 15.04
+ 15.04
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+ 14.26
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+ 18.00
+ 18.00
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+ virginica
+ 20.10
+ 20.10
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+ virginica
+ 15.75
+ 15.75
- 1
- 1
- 2
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+ virginica
+ 19.50
+ 19.50
- obs3
- 0
- 1
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+ virginica
+ 21.08
+ 21.08
- 3
- 0
- 5
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+ virginica
+ 17.70
+ 17.70
+150 rows × 7 columns
-
-
-Addition#
-It is typical to create a new column from existing columns.
-In this example, a new column (or field) is created by summing x and y:
-
-
-df['x_plus_y'] = df.x + df.y
-
-
-
-
-df
+iris_df.sort_index(axis=0, ascending=False)
@@ -2844,149 +3548,155 @@ Addition
x
- y
- is_label
- x_plus_y
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
- 0
- 0
- 1
- True
- 1
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+ virginica
+ 17.70
+ 17.70
- 1
- 2
- 1
- False
- 3
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+ virginica
+ 21.08
+ 21.08
- obs3
- 1
- 0
- False
- 1
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+ virginica
+ 19.50
+ 19.50
- 3
- 5
- 0
- False
- 5
-
-
-
-
-
-Note that:
-
-The left side has form: DataFrame name, bracket notation, new column name
The assignment operator = is used
The right side contains an expression; here, two df columns are summed
-Bracket notation also works on the fields, but it’s more typing:
-
-
-df['x_plus_y'] = df['x'] + df['y']
-df
-
-
-
-
-
-
-
-
-
- x
- y
- is_label
- x_plus_y
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+ virginica
+ 15.75
+ 15.75
-
-
- 0
- 0
- 1
- True
- 1
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+ virginica
+ 20.10
+ 20.10
- 1
- 2
- 1
- False
- 3
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
- obs3
- 1
- 0
- False
- 1
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+ 18.00
+ 18.00
3
- 5
- 0
- False
- 5
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+ 14.26
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 15.04
+ 15.04
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+ 14.70
+ 14.70
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+ 17.85
+150 rows × 7 columns
-The bracket notation must be used when assigning to a new column. This will break:
+
+
+Dealing with Missing Data#
+Pandas primarily uses the data type np.nan from NumPy to represent missing data.
-df.'x_plus_y' = df.x + df.y
-
-
-
-
- Cell In[153], line 1
- df.'x_plus_y' = df.x + df.y
- ^
-SyntaxError: invalid syntax
+import numpy as np
-
-
-Removing Columns#
-
-Using the reserverd keyword del to drop a DataFrame or single columns from the dataframe:
-df_drop = df.copy()
+df_miss = pd.DataFrame({
+ 'x':[2, np.nan, 1],
+ 'y':[np.nan, np.nan, 6]}
+)
-df_drop.head(2)
+df_miss
@@ -3011,40 +3721,36 @@ Removing Columns
+dropping missing data#
+We use the dropna() method to drop all rows with missing data in any column.
+
-# delete the column 'x'
-del df_drop['x']
-
-
-
-
-
-
-Using the method drop() to drop one or more columns. It takes takes axis parameter:
-
-axis=0 refers to rows
axis=1 refers to columns
-
+The subset parameter takes a list of column names to specify which columns should have missing values.
+
+
+Replace missing values#
+We can use fillna() to replace missing data to whatever value you like, e.g. \(0\)s.
+
+We can pass the results of an operation – for example to peform simple imputation, we can replace missing values in each column with the median value of the respective column:
-# Now a particular observation
-df_drop = df_drop.drop([0], axis=0)
-df_drop
+df_filled = df_miss.fillna(df_miss.median())
+
+
+
+
+
+
+df_filled
@@ -3187,21 +3871,25 @@ Removing Columns
x
y
- 1
- 1
+ 0
+ 2.0
+ 6.0
- obs3
- 0
+ 1
+ 1.5
+ 6.0
- 3
- 0
+ 2
+ 1.0
+ 6.0
@@ -3262,23 +3950,22 @@ Removing ColumnsSummarizing data
-
- Selection and Indexing
-- By Index
-- By label
+- Working with columns
+
+- Working with the dataframe as a whole
- Masking
-- Sorting and Ranking
-.sort_index()- Dealing with Missing Data
-.dropna().fillna()- Column selection, addition, deletion
diff --git a/chapters/module-4/044-PandasII-exploration.html b/chapters/module-4/044-PandasII-exploration.html
new file mode 100644
index 0000000..a0435d9
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+++ b/chapters/module-4/044-PandasII-exploration.html
@@ -0,0 +1,2891 @@
+
+
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+
Pandas: Data Exploration — DS-1002 Programming for Data Science
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+ Skip to main content
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+ Pandas: Data Exploration
+
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+ Contents
+
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+
+Pandas: Data Exploration#
+Pandas is extremely useful for all the steps from data acquisition to analysis. Between these stages, an essential process involves exploring and processing the data to ensure it’s ready for meaningful insights.
+
+In this lesson, you will learn how to use Pandas to get a first idea about your data. Specifically, we will cover:
+
+Use Pandas for data inspection and exploration.
Basic selection, indexing and slicing.
Basic data cleaning.
+
+
+# import dependencies
+import pandas as pd
+
+
+
+
+We will work with the following dataset:
+
+
+iris_df = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/refs/heads/master/iris.csv")
+iris_df
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+ virginica
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+ virginica
+
+
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+ virginica
+
+
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+ virginica
+
+
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+ virginica
+
+
+
+150 rows × 5 columns
+
+
+Check that we have a dataframe:
+
+
+type(iris_df)
+
+
+
+
+pandas.core.frame.DataFrame
+
+
+
+
+Try it yourself with Practice Exercise 1!
+
+Data Inspection#
+
+Exploring dataframe’s structure#
+
+head(): returns the first records in dataframe.
+
+
+iris_df.head()
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+
+
+
+
+
+
+
+# You can specify how many rows to show
+iris_df.head(10)
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+
+
+ 5
+ 5.4
+ 3.9
+ 1.7
+ 0.4
+ setosa
+
+
+ 6
+ 4.6
+ 3.4
+ 1.4
+ 0.3
+ setosa
+
+
+ 7
+ 5.0
+ 3.4
+ 1.5
+ 0.2
+ setosa
+
+
+ 8
+ 4.4
+ 2.9
+ 1.4
+ 0.2
+ setosa
+
+
+ 9
+ 4.9
+ 3.1
+ 1.5
+ 0.1
+ setosa
+
+
+
+
+
+
+tail(): returns the last records in dataframe.
+
+
+iris_df.tail()
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+ virginica
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+ virginica
+
+
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+ virginica
+
+
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+ virginica
+
+
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+ virginica
+
+
+
+
+
+
+
+# Again, we can can specify how many rows to show
+iris_df.tail(10)
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ 140
+ 6.7
+ 3.1
+ 5.6
+ 2.4
+ virginica
+
+
+ 141
+ 6.9
+ 3.1
+ 5.1
+ 2.3
+ virginica
+
+
+ 142
+ 5.8
+ 2.7
+ 5.1
+ 1.9
+ virginica
+
+
+ 143
+ 6.8
+ 3.2
+ 5.9
+ 2.3
+ virginica
+
+
+ 144
+ 6.7
+ 3.3
+ 5.7
+ 2.5
+ virginica
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+ virginica
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+ virginica
+
+
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+ virginica
+
+
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+ virginica
+
+
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+ virginica
+
+
+
+
+
+
+dtypes: returns the data types of each column.
+
+
+iris_df.dtypes
+
+
+
+
+sepal_length float64
+sepal_width float64
+petal_length float64
+petal_width float64
+species object
+dtype: object
+
+
+
+
+
+shape: As with NumPy, the shape of the dataframe (number of rows, number of columns).
+
+
+iris_df.shape
+
+
+
+
+(150, 5)
+
+
+
+
+You can also use the built-in funciton len() to obtain the row (record) count.
+
+
+len(iris_df)
+
+
+
+
+150
+
+
+
+
+
+columns: contains the column names.
+
+
+iris_df.columns
+
+
+
+
+Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',
+ 'species'],
+ dtype='object')
+
+
+
+
+
+info(): prints information about the dataframe including the index dtype and columns, non-null values and memory usage.
+
+
+iris_df.info()
+
+
+
+
+<class 'pandas.core.frame.DataFrame'>
+RangeIndex: 150 entries, 0 to 149
+Data columns (total 5 columns):
+ # Column Non-Null Count Dtype
+--- ------ -------------- -----
+ 0 sepal_length 150 non-null float64
+ 1 sepal_width 150 non-null float64
+ 2 petal_length 150 non-null float64
+ 3 petal_width 150 non-null float64
+ 4 species 150 non-null object
+dtypes: float64(4), object(1)
+memory usage: 6.0+ KB
+
+
+
+
+Try it yourself with Practice Exercise 2!
+
+
+Summarizing data#
+
+describe(): summarizes the central tendency (i.e. mean), dispersion (i.e. standard deviation) and shape of a dataset’s distribution.
+
+
+iris_df.describe()
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+
+
+
+
+ count
+ 150.000000
+ 150.000000
+ 150.000000
+ 150.000000
+
+
+ mean
+ 5.843333
+ 3.057333
+ 3.758000
+ 1.199333
+
+
+ std
+ 0.828066
+ 0.435866
+ 1.765298
+ 0.762238
+
+
+ min
+ 4.300000
+ 2.000000
+ 1.000000
+ 0.100000
+
+
+ 25%
+ 5.100000
+ 2.800000
+ 1.600000
+ 0.300000
+
+
+ 50%
+ 5.800000
+ 3.000000
+ 4.350000
+ 1.300000
+
+
+ 75%
+ 6.400000
+ 3.300000
+ 5.100000
+ 1.800000
+
+
+ max
+ 7.900000
+ 4.400000
+ 6.900000
+ 2.500000
+
+
+
+
+
+
+
+# if you prefer the columns in the rows
+iris_df.describe().T
+
+
+
+
+
+
+
+
+
+ count
+ mean
+ std
+ min
+ 25%
+ 50%
+ 75%
+ max
+
+
+
+
+ sepal_length
+ 150.0
+ 5.843333
+ 0.828066
+ 4.3
+ 5.1
+ 5.80
+ 6.4
+ 7.9
+
+
+ sepal_width
+ 150.0
+ 3.057333
+ 0.435866
+ 2.0
+ 2.8
+ 3.00
+ 3.3
+ 4.4
+
+
+ petal_length
+ 150.0
+ 3.758000
+ 1.765298
+ 1.0
+ 1.6
+ 4.35
+ 5.1
+ 6.9
+
+
+ petal_width
+ 150.0
+ 1.199333
+ 0.762238
+ 0.1
+ 0.3
+ 1.30
+ 1.8
+ 2.5
+
+
+
+
+
+By default, if the dataframe contains mixed type data (numeric and categorical), it will summarize only the numeric data.
+If you want to summarize the categorical data, this needs to happen separately.
+
+
+iris_df[["species"]].describe()
+
+
+
+
+
+
+
+
+
+ species
+
+
+
+
+ count
+ 150
+
+
+ unique
+ 3
+
+
+ top
+ setosa
+
+
+ freq
+ 50
+
+
+
+
+
+
+
+iris_df.sepal_length.describe()
+
+
+
+
+count 150.000000
+mean 5.843333
+std 0.828066
+min 4.300000
+25% 5.100000
+50% 5.800000
+75% 6.400000
+max 7.900000
+Name: sepal_length, dtype: float64
+
+
+
+
+
+value_counts(): returns the frequency for each distinct value. Arguments give the ability to sort by count or index, normalize, and more. Look at its documentation for further details.
+
+
+iris_df.species.value_counts()
+
+
+
+
+setosa 50
+versicolor 50
+virginica 50
+Name: species, dtype: int64
+
+
+
+
+Show percentages instead of counts
+
+
+iris_df.species.value_counts(normalize=True)
+
+
+
+
+setosa 0.333333
+versicolor 0.333333
+virginica 0.333333
+Name: species, dtype: float64
+
+
+
+
+
+corr(): returns the correlation between numeric columns.
+
+
+iris_df.corr()
+
+
+
+
+/tmp/ipykernel_17540/1934569051.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
+ iris_df.corr()
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+
+
+
+
+ sepal_length
+ 1.000000
+ -0.117570
+ 0.871754
+ 0.817941
+
+
+ sepal_width
+ -0.117570
+ 1.000000
+ -0.428440
+ -0.366126
+
+
+ petal_length
+ 0.871754
+ -0.428440
+ 1.000000
+ 0.962865
+
+
+ petal_width
+ 0.817941
+ -0.366126
+ 0.962865
+ 1.000000
+
+
+
+
+
+Correlation can be computed on two fields by subsetting on them:
+
+
+iris_df[['sepal_length','petal_length']].corr()
+
+
+
+
+
+
+
+
+
+ sepal_length
+ petal_length
+
+
+
+
+ sepal_length
+ 1.000000
+ 0.871754
+
+
+ petal_length
+ 0.871754
+ 1.000000
+
+
+
+
+
+
+
+iris_df[['sepal_length','petal_length','sepal_width']].corr()
+
+
+
+
+
+
+
+
+
+ sepal_length
+ petal_length
+ sepal_width
+
+
+
+
+ sepal_length
+ 1.000000
+ 0.871754
+ -0.11757
+
+
+ petal_length
+ 0.871754
+ 1.000000
+ -0.42844
+
+
+ sepal_width
+ -0.117570
+ -0.428440
+ 1.00000
+
+
+
+
+
+Try it yourself with Practice Exercise 3!
+
+
+Visualizing data#
+Pandas dataframes have a plot method, which enables quick data visualization.
+This method is built on Matplotlib, the primary library for creating visualizations in Python. Another important library for this purpose is Seaborn, which works seamlessly with DataFrames and produces professional, visually appealing results.
+
+
+# hist to display continuous data
+iris_df.plot(y="sepal_length", kind="hist")
+
+
+
+
+<Axes: ylabel='Frequency'>
+
+
+
+
+
+
+
+# barplots to show categorical data
+iris_df.species.value_counts().plot(kind="bar")
+
+
+
+
+<Axes: >
+
+
+
+
+
+
+
+# or a pie chart
+iris_df.species.value_counts().plot(kind="pie")
+
+
+
+
+<Axes: ylabel='species'>
+
+
+
+
+
+
+
+# scatterplots to display the relation between continuous data
+iris_df.plot(x="sepal_length", y="petal_length", kind="scatter")
+
+
+
+
+<Axes: xlabel='sepal_length', ylabel='petal_length'>
+
+
+
+
+
+The relationship between a categorical and a continuous variable is often visualized using a boxplot.
+
+
+iris_df.plot(x="species", y="petal_length", kind="box")
+
+
+
+
+<Axes: >
+
+
+
+
+
+But this does not work as expected… We have to use boxplot:
+
+
+iris_df.boxplot("petal_length", by="species")
+
+
+
+
+<Axes: title={'center': 'petal_length'}, xlabel='species'>
+
+
+
+
+
+Try it yourself with Practice Exercise 4!
+
+
+
+Basic Indexing and Selection#
+
+Quick access to columns by name#
+
+Bracket notation: As in dictionaries, use [] (single or double; see below), and inside the name of the column(s), which must be a string.
+
+
+# Single brackets give a series
+iris_df['sepal_length'], type(iris_df['sepal_length'])
+
+
+
+
+(0 5.1
+ 1 4.9
+ 2 4.7
+ 3 4.6
+ 4 5.0
+ ...
+ 145 6.7
+ 146 6.3
+ 147 6.5
+ 148 6.2
+ 149 5.9
+ Name: sepal_length, Length: 150, dtype: float64,
+ pandas.core.series.Series)
+
+
+
+
+
+
+# double bracket gives you the selected column as new dataframe
+iris_df[['sepal_length']], type(iris_df[['sepal_length']])
+
+
+
+
+( sepal_length
+ 0 5.1
+ 1 4.9
+ 2 4.7
+ 3 4.6
+ 4 5.0
+ .. ...
+ 145 6.7
+ 146 6.3
+ 147 6.5
+ 148 6.2
+ 149 5.9
+
+ [150 rows x 1 columns],
+ pandas.core.frame.DataFrame)
+
+
+
+
+This notation allows you to select multiple columns. To do this, use double brackets [[.., ..]], ensuring that the returned object is a dataframe:
+
+
+iris_df[['sepal_length', 'petal_length']]
+
+
+
+
+
+
+
+
+
+ sepal_length
+ petal_length
+
+
+
+
+ 0
+ 5.1
+ 1.4
+
+
+ 1
+ 4.9
+ 1.4
+
+
+ 2
+ 4.7
+ 1.3
+
+
+ 3
+ 4.6
+ 1.5
+
+
+ 4
+ 5.0
+ 1.4
+
+
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 5.2
+
+
+ 146
+ 6.3
+ 5.0
+
+
+ 147
+ 6.5
+ 5.2
+
+
+ 148
+ 6.2
+ 5.4
+
+
+ 149
+ 5.9
+ 5.1
+
+
+
+150 rows × 2 columns
+
+
+
+Dot notation: Here columns are object attributes.
+
+
+iris_df.sepal_length, type(iris_df.sepal_length)
+
+
+
+
+(0 5.1
+ 1 4.9
+ 2 4.7
+ 3 4.6
+ 4 5.0
+ ...
+ 145 6.7
+ 146 6.3
+ 147 6.5
+ 148 6.2
+ 149 5.9
+ Name: sepal_length, Length: 150, dtype: float64,
+ pandas.core.series.Series)
+
+
+
+
+Dot notation is very convenient, since as object attributes they can be tab-completed in various editing environments.
+But:
+
+It only works if the column names are not reserved keywords.
It can not be used when creating a new column (see below).
It allows you to select just one column.
+
+
+Selecting Data by Position: iloc[]#
+We can use iloc[] to extract rows and columns using indexes.
+
+
+# This fetches row 3, and all columns:
+iris_df.iloc[2]
+
+
+
+
+sepal_length 4.7
+sepal_width 3.2
+petal_length 1.3
+petal_width 0.2
+species setosa
+Name: 2, dtype: object
+
+
+
+
+
+
+# Similar to
+iris_df.iloc[2, :]
+
+
+
+
+sepal_length 4.7
+sepal_width 3.2
+petal_length 1.3
+petal_width 0.2
+species setosa
+Name: 2, dtype: object
+
+
+
+
+fetch rows with indices 1,2 (the right endpoint is exclusive), and all columns.
+
+
+iris_df.iloc[1:3]
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+
+
+
+
+
+fetch rows with indices 1,2 and first three columns (positions 0, 1, 2)
+
+
+iris_df.iloc[1:3, 0:3]
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+
+
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+
+
+
+
+
+You can apply slices to column names too. You don’t need .iloc[] here.
+
+
+iris_df.columns[0:3]
+
+
+
+
+Index(['sepal_length', 'sepal_width', 'petal_length'], dtype='object')
+
+
+
+
+
+
+Selecting Data by Label: loc[]#
+We can select by row and column labels using the loc[] method.
+
+
+# Here we ask for rows with labels (indexes) 1-3
+iris_df.loc[1:3]
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+
+
+
+
+
+Look at the difference with respect to iloc:
+
+
+iris_df.iloc[1:3]
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+
+
+
+
+
+To make the difference between the two approaches more apparent, let’s create a copy of the DataFrame with redefined index labels:
+
+
+iris_copy = iris_df.copy() # Create a copy to not affect the original dataframe
+iris_copy.index = [f"obs{ii}" for ii in iris_copy.index]
+
+iris_copy.head()
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ obs0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+
+
+ obs1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+
+
+ obs2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+
+
+ obs3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+
+
+ obs4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+
+
+
+
+
+
+
+# This is still Ok
+iris_copy.iloc[1:3,:]
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ obs1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+
+
+ obs2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+
+
+
+
+
+
+
+# But this will give an error
+iris_copy.loc[1:3,:]
+
+
+
+
+---------------------------------------------------------------------------
+TypeError Traceback (most recent call last)
+Cell In[41], line 2
+ 1 # But this will give an error
+----> 2 iris_copy.loc[1:3,:]
+
+File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1067, in _LocationIndexer.__getitem__(self, key)
+ 1065 if self._is_scalar_access(key):
+ 1066 return self.obj._get_value(*key, takeable=self._takeable)
+-> 1067 return self._getitem_tuple(key)
+ 1068 else:
+ 1069 # we by definition only have the 0th axis
+ 1070 axis = self.axis or 0
+
+File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1256, in _LocIndexer._getitem_tuple(self, tup)
+ 1253 if self._multi_take_opportunity(tup):
+ 1254 return self._multi_take(tup)
+-> 1256 return self._getitem_tuple_same_dim(tup)
+
+File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:924, in _LocationIndexer._getitem_tuple_same_dim(self, tup)
+ 921 if com.is_null_slice(key):
+ 922 continue
+--> 924 retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
+ 925 # We should never have retval.ndim < self.ndim, as that should
+ 926 # be handled by the _getitem_lowerdim call above.
+ 927 assert retval.ndim == self.ndim
+
+File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1290, in _LocIndexer._getitem_axis(self, key, axis)
+ 1288 if isinstance(key, slice):
+ 1289 self._validate_key(key, axis)
+-> 1290 return self._get_slice_axis(key, axis=axis)
+ 1291 elif com.is_bool_indexer(key):
+ 1292 return self._getbool_axis(key, axis=axis)
+
+File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1324, in _LocIndexer._get_slice_axis(self, slice_obj, axis)
+ 1321 return obj.copy(deep=False)
+ 1323 labels = obj._get_axis(axis)
+-> 1324 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
+ 1326 if isinstance(indexer, slice):
+ 1327 return self.obj._slice(indexer, axis=axis)
+
+File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6559, in Index.slice_indexer(self, start, end, step, kind)
+ 6516 """
+ 6517 Compute the slice indexer for input labels and step.
+ 6518
+ (...)
+ 6555 slice(1, 3, None)
+ 6556 """
+ 6557 self._deprecated_arg(kind, "kind", "slice_indexer")
+-> 6559 start_slice, end_slice = self.slice_locs(start, end, step=step)
+ 6561 # return a slice
+ 6562 if not is_scalar(start_slice):
+
+File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6767, in Index.slice_locs(self, start, end, step, kind)
+ 6765 start_slice = None
+ 6766 if start is not None:
+-> 6767 start_slice = self.get_slice_bound(start, "left")
+ 6768 if start_slice is None:
+ 6769 start_slice = 0
+
+File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6676, in Index.get_slice_bound(self, label, side, kind)
+ 6672 original_label = label
+ 6674 # For datetime indices label may be a string that has to be converted
+ 6675 # to datetime boundary according to its resolution.
+-> 6676 label = self._maybe_cast_slice_bound(label, side)
+ 6678 # we need to look up the label
+ 6679 try:
+
+File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6623, in Index._maybe_cast_slice_bound(self, label, side, kind)
+ 6618 # We are a plain index here (sub-class override this method if they
+ 6619 # wish to have special treatment for floats/ints, e.g. Float64Index and
+ 6620 # datetimelike Indexes
+ 6621 # reject them, if index does not contain label
+ 6622 if (is_float(label) or is_integer(label)) and label not in self:
+-> 6623 raise self._invalid_indexer("slice", label)
+ 6625 return label
+
+TypeError: cannot do slice indexing on Index with these indexers [1] of type int
+
+
+
+
+
+
+# With loc[] labels need to be passed
+iris_copy.loc[["obs1", "obs2", "obs3"]]
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ obs1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+
+
+ obs2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+
+
+ obs3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+
+
+
+
+
+Subset on columns with column name (as a string) or list of strings
+
+
+iris_df.loc[1:3, ['sepal_length','petal_width']], iris_copy.loc[["obs1", "obs2", "obs3"], ['sepal_length','petal_width']]
+
+
+
+
+( sepal_length petal_width
+ 1 4.9 0.2
+ 2 4.7 0.2
+ 3 4.6 0.2,
+ sepal_length petal_width
+ obs1 4.9 0.2
+ obs2 4.7 0.2
+ obs3 4.6 0.2)
+
+
+
+
+Select all rows, specific columns
+
+
+iris_df.loc[:, ['sepal_length','petal_width']]
+
+
+
+
+
+
+
+
+
+ sepal_length
+ petal_width
+
+
+
+
+ 0
+ 5.1
+ 0.2
+
+
+ 1
+ 4.9
+ 0.2
+
+
+ 2
+ 4.7
+ 0.2
+
+
+ 3
+ 4.6
+ 0.2
+
+
+ 4
+ 5.0
+ 0.2
+
+
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 2.3
+
+
+ 146
+ 6.3
+ 1.9
+
+
+ 147
+ 6.5
+ 2.0
+
+
+ 148
+ 6.2
+ 2.3
+
+
+ 149
+ 5.9
+ 1.8
+
+
+
+150 rows × 2 columns
+
+
+Try it yourself with Practice Exercise 5!
+
+
+
+Basic data cleaning#
+Pandas primarily uses the data type np.nan from NumPy to represent missing data.
+
+
+import numpy as np
+
+
+
+
+
+
+df_miss = pd.DataFrame({
+ 'x':[2, np.nan, 1],
+ 'y':[np.nan, np.nan, 6]}
+)
+
+
+
+
+
+
+df_miss
+
+
+
+
+
+
+
+
+
+ x
+ y
+
+
+
+
+ 0
+ 2.0
+ NaN
+
+
+ 1
+ NaN
+ NaN
+
+
+ 2
+ 1.0
+ 6.0
+
+
+
+
+
+
+Drop missing data#
+We use the dropna() method to drop all rows with missing data in any column. Look at its documentation for further details.
+
+
+df_drop_all = df_miss.dropna()
+df_drop_all
+
+
+
+
+
+
+
+
+
+ x
+ y
+
+
+
+
+ 2
+ 1.0
+ 6.0
+
+
+
+
+
+The subset parameter takes a list of column names to specify which columns should have missing values.
+
+
+df_drop_x = df_miss.dropna(subset=['x'])
+df_drop_x
+
+
+
+
+
+
+
+
+
+ x
+ y
+
+
+
+
+ 0
+ 2.0
+ NaN
+
+
+ 2
+ 1.0
+ 6.0
+
+
+
+
+
+
+
+Impute missing values#
+We can use fillna() to replace missing data to whatever value you like, e.g. \(0\)s. Look at its documentation for further details.
+We can pass the results of an operation – for example to peform simple imputation, we can replace missing values in each column with the median value of the respective column:
+
+
+df_filled = df_miss.fillna(df_miss.median())
+
+
+
+
+
+
+df_filled
+
+
+
+
+
+
+
+
+
+ x
+ y
+
+
+
+
+ 0
+ 2.0
+ 6.0
+
+
+ 1
+ 1.5
+ 6.0
+
+
+ 2
+ 1.0
+ 6.0
+
+
+
+
+
+Try it yourself with Practice Exercise 6!
+
+
+
+Practice exercises#
+
+
+Exercise 38
+
+1- Go to https://mockaroo.com and generate 1000-record data set. You can drop the ip_address column, and add one called age that is populated by plain whole numbers. Allow 5% of values in this column to be blank. Be sure to format it as a CSV and to include the header row. Upload the dataset to your Rivanna space. Import the dataset into this notebook. Store it in a variable mock_df.
+
+
+
+
+# Your answers from here
+
+
+
+
+
+
+Exercise 39
+
+2- Explore mock_df using the methods introduced in this notebook.
+
+
+
+
+# Your answers from here
+
+
+
+
+
+
+Exercise 40
+
+3- Summarize the data in mock_df using the methods introduced in this notebook.
+
+
+
+
+# Your answers from here
+
+
+
+
+
+
+Exercise 41
+
+4- Try to visualize some of data in mock_df using the methods introduced in this notebook.
+
+
+
+
+# Your answers from here
+
+
+
+
+
+
+Exercise 42
+
+5- Play around with selecting columns and rows of mock_df using loc and iloc
+
+
+
+
+# Your answers from here
+
+
+
+
+
+
+Exercise 43
+
+6- Drop observations in blank values in mock_df. Then, instead of dropping these values, impute them in any way you like (e.g. by replacing them with the mean)
+
+
+
+
+# Your answers from here
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
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+
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+
+ PandasIII: Data Manipulation — DS-1002 Programming for Data Science
+
+
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+
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+
+
+
+
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+
+
+
+
+
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+
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+
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+
+ Skip to main content
+
+
+
+
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+
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+
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+
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+
+
+
+
+ PandasIII: Data Manipulation
+
+
+
+
+
+ Contents
+
+
+
+
+
+
+
+
+
+
+
+
+PandasIII: Data Manipulation#
+
+In the previous lesson, we studied how Pandas can be used to learn about your data through inspection and exploration.
+In this lesson, you’ll learn how Pandas can help you process the data to prepare them for analysis.
+Specifically, we will cover:
+
+Advance filtering and subsetting.
The creation and removal of columns.
Transforming data through functions.
Sorting data.
+
+
+import pandas as pd
+
+
+
+
+
+Advanced Filtering and Subsetting#
+
+
+iris_df = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/refs/heads/master/iris.csv")
+iris_df
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+ virginica
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+ virginica
+
+
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+ virginica
+
+
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+ virginica
+
+
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+ virginica
+
+
+
+150 rows × 5 columns
+
+
+
+Boolean Filtering#
+It is very common to subset a dataframe based on some condition on the data.
+Like with NumPy, Pandas also knows what to do if you pass a boolean structure.
+
+
+iris_df.sepal_length >= 7.5
+
+
+
+
+0 False
+1 False
+2 False
+3 False
+4 False
+ ...
+145 False
+146 False
+147 False
+148 False
+149 False
+Name: sepal_length, Length: 150, dtype: bool
+
+
+
+
+
+
+# Rows satisfying the above condition
+iris_df.loc[iris_df.sepal_length >= 7.5,:]
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ 105
+ 7.6
+ 3.0
+ 6.6
+ 2.1
+ virginica
+
+
+ 117
+ 7.7
+ 3.8
+ 6.7
+ 2.2
+ virginica
+
+
+ 118
+ 7.7
+ 2.6
+ 6.9
+ 2.3
+ virginica
+
+
+ 122
+ 7.7
+ 2.8
+ 6.7
+ 2.0
+ virginica
+
+
+ 131
+ 7.9
+ 3.8
+ 6.4
+ 2.0
+ virginica
+
+
+ 135
+ 7.7
+ 3.0
+ 6.1
+ 2.3
+ virginica
+
+
+
+
+
+We can combine more than one conditions, similarly to NumPy:
+
+
+iris_df.loc[(iris_df['sepal_length' ]>= 4.5) & (iris_df['sepal_length'] <= 4.7),:]
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+
+
+ 6
+ 4.6
+ 3.4
+ 1.4
+ 0.3
+ setosa
+
+
+ 22
+ 4.6
+ 3.6
+ 1.0
+ 0.2
+ setosa
+
+
+ 29
+ 4.7
+ 3.2
+ 1.6
+ 0.2
+ setosa
+
+
+ 41
+ 4.5
+ 2.3
+ 1.3
+ 0.3
+ setosa
+
+
+ 47
+ 4.6
+ 3.2
+ 1.4
+ 0.2
+ setosa
+
+
+
+
+
+
+
+Masking#
+Here’s an example of masking using boolean conditions passed to the dataframe selector:
+Here are the values for the feature sepal length:
+
+
+iris_df.sepal_length.values
+
+
+
+
+array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,
+ 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,
+ 5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,
+ 5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. , 7. , 6.4,
+ 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5. , 5.9, 6. , 6.1, 5.6,
+ 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7,
+ 6. , 5.7, 5.5, 5.5, 5.8, 6. , 5.4, 6. , 6.7, 6.3, 5.6, 5.5, 5.5,
+ 6.1, 5.8, 5. , 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3,
+ 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5,
+ 7.7, 7.7, 6. , 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2,
+ 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6. , 6.9, 6.7, 6.9, 5.8,
+ 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9])
+
+
+
+
+And here are the boolean values generated by applying a comparison operator to those values:
+
+
+mask = iris_df.sepal_length >= 7.5
+
+
+
+
+
+
+mask.values
+
+
+
+
+array([False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False, True, False, False,
+ False, False, False, False, False, False, False, False, False,
+ True, True, False, False, False, True, False, False, False,
+ False, False, False, False, False, True, False, False, False,
+ True, False, False, False, False, False, False, False, False,
+ False, False, False, False, False, False])
+
+
+
+
+The two sets of values have the same shape.
+We can now overlay the logical values over the numeric ones and keep only what is True:
+
+
+iris_df.sepal_length[mask].values
+
+
+
+
+array([7.6, 7.7, 7.7, 7.7, 7.9, 7.7])
+
+
+
+
+
+
+
+Creating columns#
+It is typical to create new columns from existing columns.
+
+Using brackets []#
+Like in dictionaries, you can create a new entry (column) in the dataframe using [].
+In this example, a new column (or field) is created by multiplying summing sepal_length with sepal_width:
+
+
+iris_df['sepal_volume'] = iris_df.sepal_length * iris_df.sepal_width
+
+iris_df.head()
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+ 14.70
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 15.04
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+ 18.00
+
+
+
+
+
+Note that:
+
+The left side has form: DataFrame name, bracket notation, new column name
The assignment operator = is used
The right side contains an expression; here, two df columns are multiplied together
+Bracket notation also works on the fields, but it’s more typing:
+
+
+iris_df['sepal_volume_2'] = iris_df['sepal_length'] * iris_df['sepal_width']
+
+iris_df.head()
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+ 17.85
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+ 14.70
+ 14.70
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 15.04
+ 15.04
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+ 14.26
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+ 18.00
+ 18.00
+
+
+
+
+
+The bracket notation must be used when assigning to a new column. This will break:
+
+
+iris_df.'sepal_volume_3' = iris_df.sepal_length + iris_df.sepal_width
+
+
+
+
+ Cell In[12], line 1
+ iris_df.'sepal_volume_3' = iris_df.sepal_length + iris_df.sepal_width
+ ^
+SyntaxError: invalid syntax
+
+
+
+
+
+
+iris_df.sepal_volume_3 = iris_df.sepal_length + iris_df.sepal_width
+
+
+
+
+/tmp/ipykernel_21005/2179810851.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+ iris_df.sepal_volume_3 = iris_df.sepal_length + iris_df.sepal_width
+
+
+
+
+
+
+Using assign#
+This method also allows you to create new columns in a given dataset.
+it is very useful when you want to create a new dataframe while maintaining the original as it was.
+
+
+iris_df
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+ 17.85
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+ 14.70
+ 14.70
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 15.04
+ 15.04
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+ 14.26
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+ 18.00
+ 18.00
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+ virginica
+ 20.10
+ 20.10
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+ virginica
+ 15.75
+ 15.75
+
+
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+ virginica
+ 19.50
+ 19.50
+
+
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+ virginica
+ 21.08
+ 21.08
+
+
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+ virginica
+ 17.70
+ 17.70
+
+
+
+150 rows × 7 columns
+
+
+
+
+iris_df.assign(sepal_volume_4 = iris_df["sepal_length"] * iris_df["sepal_width"])
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
+ sepal_volume_4
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+ 17.85
+ 17.85
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+ 14.70
+ 14.70
+ 14.70
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 15.04
+ 15.04
+ 15.04
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+ 14.26
+ 14.26
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+ 18.00
+ 18.00
+ 18.00
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+ virginica
+ 20.10
+ 20.10
+ 20.10
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+ virginica
+ 15.75
+ 15.75
+ 15.75
+
+
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+ virginica
+ 19.50
+ 19.50
+ 19.50
+
+
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+ virginica
+ 21.08
+ 21.08
+ 21.08
+
+
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+ virginica
+ 17.70
+ 17.70
+ 17.70
+
+
+
+150 rows × 8 columns
+
+
+
+
+
+Removing columns#
+
+Using the reserverd keyword del to drop a DataFrame or single columns from the dataframe:
+
+
+iris_df_drop = iris_df.copy()
+
+
+
+
+The history saving thread hit an unexpected error (OperationalError('attempt to write a readonly database')).History will not be written to the database.
+
+
+
+
+
+
+iris_df_drop.head(2)
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+ 17.85
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+ 14.70
+ 14.70
+
+
+
+
+
+
+
+# delete the column 'x'
+del iris_df_drop['sepal_volume_2']
+
+
+
+
+
+
+iris_df_drop
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+ 14.70
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 15.04
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+ 18.00
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+ virginica
+ 20.10
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+ virginica
+ 15.75
+
+
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+ virginica
+ 19.50
+
+
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+ virginica
+ 21.08
+
+
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+ virginica
+ 17.70
+
+
+
+150 rows × 6 columns
+
+
+
+Using the method drop() to drop one or more columns by specifying axis argument equal 1:
+
+
+# Here we drop columns
+iris_df_drop = iris_df_drop.drop(['sepal_volume', 'species'], axis=1)
+iris_df_drop
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+
+
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+
+
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+
+
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+
+
+
+150 rows × 4 columns
+
+
+Note that with this drop() method you can also drop specific observations by setting axis=0
+
+
+# Now a particular observation
+iris_df_drop = iris_df_drop.drop([0], axis=0)
+iris_df_drop
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+
+
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+
+
+ 5
+ 5.4
+ 3.9
+ 1.7
+ 0.4
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+
+
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+
+
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+
+
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+
+
+
+149 rows × 4 columns
+
+
+
+
+Transforming your data#
+Sometimes, as part of preprocessing, you may need to apply a function to transform your dataframe.
+The most straightforward way to do this is with the apply function. Let’s explore some common uses:
+
+Creation of new columns, particularly when these need to arise from a complex operation
+
+
+def my_func(x):
+ return x**3 - x**2 - x + 10
+
+iris_df["sepal_length"].apply(my_func)
+
+
+
+
+0 111.541
+1 98.739
+2 87.033
+3 81.576
+4 105.000
+ ...
+145 259.173
+146 214.057
+147 235.875
+148 203.688
+149 174.669
+Name: sepal_length, Length: 150, dtype: float64
+
+
+
+
+You can also use a lambda function, which is basically a small anonymous function that can take any number of arguments, but can only have one expression.
+lambda arguments : expression
+
+
+iris_df["sepal_length"].apply(lambda x: x**3 - x**2 - x + 10)
+
+
+
+
+0 111.541
+1 98.739
+2 87.033
+3 81.576
+4 105.000
+ ...
+145 259.173
+146 214.057
+147 235.875
+148 203.688
+149 174.669
+Name: sepal_length, Length: 150, dtype: float64
+
+
+
+
+apply takes the argument axis, which specifies the axis along which the function is applied. By default, axis is set to 0, meaning the function is applied to each column.
+
+
+import numpy as np
+iris_df[["sepal_length", "petal_length"]].apply(np.mean)
+
+
+
+
+sepal_length 5.843333
+petal_length 3.758000
+dtype: float64
+
+
+
+
+But we can change this to apply a function to each row by setting axis to 1:
+
+
+import numpy
+iris_df[["sepal_length", "petal_length"]].apply(numpy.mean, axis=1)
+
+
+
+
+0 3.25
+1 3.15
+2 3.00
+3 3.05
+4 3.20
+ ...
+145 5.95
+146 5.65
+147 5.85
+148 5.80
+149 5.50
+Length: 150, dtype: float64
+
+
+
+
+
+
+iris_df["avg_length"] = iris_df[["sepal_length", "petal_length"]].apply(numpy.mean, axis=1)
+iris_df["avg_width"] = iris_df[["sepal_width", "petal_width"]].apply(numpy.mean, axis=1)
+
+iris_df.head()
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
+ avg_length
+ avg_width
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+ 17.85
+ 3.25
+ 1.85
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+ 14.70
+ 14.70
+ 3.15
+ 1.60
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 15.04
+ 15.04
+ 3.00
+ 1.70
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+ 14.26
+ 3.05
+ 1.65
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+ 18.00
+ 18.00
+ 3.20
+ 1.90
+
+
+
+
+
+
+
+Sorting Data#
+
+By values: sort_values()#
+You can customize this sorting with the following parameters:
+
+by parameter takes string or list of strings
ascending takes True or False
inplace will save sorted values into the df
+Look at its documentation for further details.
+
+
+iris_df.sort_values(by=['sepal_length','petal_width'])
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
+ avg_length
+ avg_width
+
+
+
+
+ 13
+ 4.3
+ 3.0
+ 1.1
+ 0.1
+ setosa
+ 12.90
+ 12.90
+ 2.70
+ 1.55
+
+
+ 8
+ 4.4
+ 2.9
+ 1.4
+ 0.2
+ setosa
+ 12.76
+ 12.76
+ 2.90
+ 1.55
+
+
+ 38
+ 4.4
+ 3.0
+ 1.3
+ 0.2
+ setosa
+ 13.20
+ 13.20
+ 2.85
+ 1.60
+
+
+ 42
+ 4.4
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 14.08
+ 14.08
+ 2.85
+ 1.70
+
+
+ 41
+ 4.5
+ 2.3
+ 1.3
+ 0.3
+ setosa
+ 10.35
+ 10.35
+ 2.90
+ 1.30
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 122
+ 7.7
+ 2.8
+ 6.7
+ 2.0
+ virginica
+ 21.56
+ 21.56
+ 7.20
+ 2.40
+
+
+ 117
+ 7.7
+ 3.8
+ 6.7
+ 2.2
+ virginica
+ 29.26
+ 29.26
+ 7.20
+ 3.00
+
+
+ 118
+ 7.7
+ 2.6
+ 6.9
+ 2.3
+ virginica
+ 20.02
+ 20.02
+ 7.30
+ 2.45
+
+
+ 135
+ 7.7
+ 3.0
+ 6.1
+ 2.3
+ virginica
+ 23.10
+ 23.10
+ 6.90
+ 2.65
+
+
+ 131
+ 7.9
+ 3.8
+ 6.4
+ 2.0
+ virginica
+ 30.02
+ 30.02
+ 7.15
+ 2.90
+
+
+
+150 rows × 9 columns
+
+
+
+
+iris_df.sort_values(by=['sepal_length','petal_width'], ascending=False)
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
+ avg_length
+ avg_width
+
+
+
+
+ 131
+ 7.9
+ 3.8
+ 6.4
+ 2.0
+ virginica
+ 30.02
+ 30.02
+ 7.15
+ 2.90
+
+
+ 118
+ 7.7
+ 2.6
+ 6.9
+ 2.3
+ virginica
+ 20.02
+ 20.02
+ 7.30
+ 2.45
+
+
+ 135
+ 7.7
+ 3.0
+ 6.1
+ 2.3
+ virginica
+ 23.10
+ 23.10
+ 6.90
+ 2.65
+
+
+ 117
+ 7.7
+ 3.8
+ 6.7
+ 2.2
+ virginica
+ 29.26
+ 29.26
+ 7.20
+ 3.00
+
+
+ 122
+ 7.7
+ 2.8
+ 6.7
+ 2.0
+ virginica
+ 21.56
+ 21.56
+ 7.20
+ 2.40
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 41
+ 4.5
+ 2.3
+ 1.3
+ 0.3
+ setosa
+ 10.35
+ 10.35
+ 2.90
+ 1.30
+
+
+ 8
+ 4.4
+ 2.9
+ 1.4
+ 0.2
+ setosa
+ 12.76
+ 12.76
+ 2.90
+ 1.55
+
+
+ 38
+ 4.4
+ 3.0
+ 1.3
+ 0.2
+ setosa
+ 13.20
+ 13.20
+ 2.85
+ 1.60
+
+
+ 42
+ 4.4
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 14.08
+ 14.08
+ 2.85
+ 1.70
+
+
+ 13
+ 4.3
+ 3.0
+ 1.1
+ 0.1
+ setosa
+ 12.90
+ 12.90
+ 2.70
+ 1.55
+
+
+
+150 rows × 9 columns
+
+
+
+
+By index: sort_index()#
+You can customize this sorting with the following parameters:
+- `axis` along which to sort. The value 0 identifies the rows, and 1 identifies the columns.
+- `ascending` takes True or False
+- `inplace` will save sorted values into the df
+
+
+Look at its documentation for further details.
+
+
+iris_df.sort_index(axis=0)
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
+ avg_length
+ avg_width
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+ 17.85
+ 3.25
+ 1.85
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+ 14.70
+ 14.70
+ 3.15
+ 1.60
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 15.04
+ 15.04
+ 3.00
+ 1.70
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+ 14.26
+ 3.05
+ 1.65
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+ 18.00
+ 18.00
+ 3.20
+ 1.90
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+ virginica
+ 20.10
+ 20.10
+ 5.95
+ 2.65
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+ virginica
+ 15.75
+ 15.75
+ 5.65
+ 2.20
+
+
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+ virginica
+ 19.50
+ 19.50
+ 5.85
+ 2.50
+
+
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+ virginica
+ 21.08
+ 21.08
+ 5.80
+ 2.85
+
+
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+ virginica
+ 17.70
+ 17.70
+ 5.50
+ 2.40
+
+
+
+150 rows × 9 columns
+
+
+
+
+iris_df.sort_index(axis=0, ascending=False)
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+ sepal_volume
+ sepal_volume_2
+ avg_length
+ avg_width
+
+
+
+
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+ virginica
+ 17.70
+ 17.70
+ 5.50
+ 2.40
+
+
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+ virginica
+ 21.08
+ 21.08
+ 5.80
+ 2.85
+
+
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+ virginica
+ 19.50
+ 19.50
+ 5.85
+ 2.50
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+ virginica
+ 15.75
+ 15.75
+ 5.65
+ 2.20
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+ virginica
+ 20.10
+ 20.10
+ 5.95
+ 2.65
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+ 18.00
+ 18.00
+ 3.20
+ 1.90
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+ 14.26
+ 14.26
+ 3.05
+ 1.65
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+ 15.04
+ 15.04
+ 3.00
+ 1.70
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+ 14.70
+ 14.70
+ 3.15
+ 1.60
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+ 17.85
+ 17.85
+ 3.25
+ 1.85
+
+
+
+150 rows × 9 columns
+
+
+
+
+
+Practice exercises#
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/chapters/module-4/046-PandasIII-Merging_Concatenating_Aggregating.html b/chapters/module-4/046-PandasIII-Merging_Concatenating_Aggregating.html
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+
+
+
+
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+
+
+
+
+
+ Concatenating and Merging — DS-1002 Programming for Data Science
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Skip to main content
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Concatenating and Merging
+
+
+
+
+
+ Contents
+
+
+
+
+
+
+
+
+
+
+
+
+
+import pandas as pd
+import numpy as np
+
+
+
+
+
+
+iris_df = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/refs/heads/master/iris.csv")
+iris_df
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+ species
+
+
+
+
+ 0
+ 5.1
+ 3.5
+ 1.4
+ 0.2
+ setosa
+
+
+ 1
+ 4.9
+ 3.0
+ 1.4
+ 0.2
+ setosa
+
+
+ 2
+ 4.7
+ 3.2
+ 1.3
+ 0.2
+ setosa
+
+
+ 3
+ 4.6
+ 3.1
+ 1.5
+ 0.2
+ setosa
+
+
+ 4
+ 5.0
+ 3.6
+ 1.4
+ 0.2
+ setosa
+
+
+ ...
+ ...
+ ...
+ ...
+ ...
+ ...
+
+
+ 145
+ 6.7
+ 3.0
+ 5.2
+ 2.3
+ virginica
+
+
+ 146
+ 6.3
+ 2.5
+ 5.0
+ 1.9
+ virginica
+
+
+ 147
+ 6.5
+ 3.0
+ 5.2
+ 2.0
+ virginica
+
+
+ 148
+ 6.2
+ 3.4
+ 5.4
+ 2.3
+ virginica
+
+
+ 149
+ 5.9
+ 3.0
+ 5.1
+ 1.8
+ virginica
+
+
+
+150 rows × 5 columns
+
+
+
+Concatenating and Merging#
+
+pd.concat()#
+Concatenate pandas objects along an axis
+
+Create two dfs and vertically stack them
+
+
+df1 = pd.DataFrame(np.random.randn(3, 4))
+df2 = pd.DataFrame(np.random.randn(3, 4))
+
+print(df1)
+print('-'*45)
+print(df2)
+
+df3 = pd.concat([df1, df2], axis=0)
+
+print('-'*45)
+print(df3)
+
+
+
+
+ 0 1 2 3
+0 1.123941 -0.257931 -0.923815 0.661988
+1 1.865679 -0.817985 0.588745 -1.448256
+2 0.409508 0.058524 1.309773 -0.873551
+---------------------------------------------
+ 0 1 2 3
+0 0.245466 -0.130858 0.127125 -0.541922
+1 -0.131013 -0.618569 -1.261011 -0.017400
+2 1.350267 0.756023 0.693795 -2.886349
+---------------------------------------------
+ 0 1 2 3
+0 1.123941 -0.257931 -0.923815 0.661988
+1 1.865679 -0.817985 0.588745 -1.448256
+2 0.409508 0.058524 1.309773 -0.873551
+0 0.245466 -0.130858 0.127125 -0.541922
+1 -0.131013 -0.618569 -1.261011 -0.017400
+2 1.350267 0.756023 0.693795 -2.886349
+
+
+
+
+Concat columns
+This assumes that the indexes represent IDs of specific things or events
+
+
+df4 = pd.concat([df1,df2], axis = 1, keys = ['foo', 'bar'])
+
+df4
+
+
+
+
+
+
+
+
+
+ foo
+ bar
+
+
+ 0
+ 1
+ 2
+ 3
+ 0
+ 1
+ 2
+ 3
+
+
+
+
+ 0
+ 1.123941
+ -0.257931
+ -0.923815
+ 0.661988
+ 0.245466
+ -0.130858
+ 0.127125
+ -0.541922
+
+
+ 1
+ 1.865679
+ -0.817985
+ 0.588745
+ -1.448256
+ -0.131013
+ -0.618569
+ -1.261011
+ -0.017400
+
+
+ 2
+ 0.409508
+ 0.058524
+ 1.309773
+ -0.873551
+ 1.350267
+ 0.756023
+ 0.693795
+ -2.886349
+
+
+
+
+
+
+
+df4.foo
+
+
+
+
+
+
+
+
+
+ 0
+ 1
+ 2
+ 3
+
+
+
+
+ 0
+ 1.123941
+ -0.257931
+ -0.923815
+ 0.661988
+
+
+ 1
+ 1.865679
+ -0.817985
+ 0.588745
+ -1.448256
+
+
+ 2
+ 0.409508
+ 0.058524
+ 1.309773
+ -0.873551
+
+
+
+
+
+
+
+df4.bar
+
+
+
+
+
+
+
+
+
+ 0
+ 1
+ 2
+ 3
+
+
+
+
+ 0
+ 0.245466
+ -0.130858
+ 0.127125
+ -0.541922
+
+
+ 1
+ -0.131013
+ -0.618569
+ -1.261011
+ -0.017400
+
+
+ 2
+ 1.350267
+ 0.756023
+ 0.693795
+ -2.886349
+
+
+
+
+
+
+
+merge()#
+SQL-style joining of tables (DataFrames)
+Important parameters include:
+
+how : type of merge {‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’}, default ‘inner’
on : names to join on
+
+Very useful!
+Create two tables, left and right. Then right join them on key.
+Right join means include all records from table on right.
+The key is used for matching up the records.
+
+
+left = pd.DataFrame({"key": ["jamie", "bill"], "lval": [15, 22]})
+right = pd.DataFrame({"key": ["jamie", "bill", "asher"], "rval": [4, 5, 8]})
+
+joined = pd.merge(left, right, on="key", how="right")
+
+print('---left')
+print(left)
+print('\n---right')
+print(right)
+print('\n---joined')
+print(joined)
+
+
+
+
+---left
+ key lval
+0 jamie 15
+1 bill 22
+
+---right
+ key rval
+0 jamie 4
+1 bill 5
+2 asher 8
+
+---joined
+ key lval rval
+0 jamie 15.0 4
+1 bill 22.0 5
+2 asher NaN 8
+
+
+
+
+
+
+
+Summary#
+
+Use join if you have shared indexes
Use merge if you do not have shared indexes
Use concat to combine based on shared indexes or columns
+
+
+Aggregation#
+Involves one or more of:
+
+splitting the data into groups
applying a function to each group
combining results
+
+.groupby()#
+Compute mean of each column, grouped (separately) by species
+
+
+iris_df.groupby("species").mean()
+
+
+
+
+
+
+
+
+
+ sepal_length
+ sepal_width
+ petal_length
+ petal_width
+
+
+ species
+
+
+
+
+ setosa
+ 5.006
+ 3.428
+ 1.462
+ 0.246
+
+
+ versicolor
+ 5.936
+ 2.770
+ 4.260
+ 1.326
+
+
+ virginica
+ 6.588
+ 2.974
+ 5.552
+ 2.026
+
+
+
+
+
+
+
+pd.pivot_table()#
+Apply a function aggfunc to selected values grouped by columns
+
+Compute mean sepal length for each species:
+
+
+pd.pivot_table(iris_df, values="sepal_length", columns=["species"], aggfunc = np.mean)
+
+
+
+
+
+
+
+
+
+ species
+ setosa
+ versicolor
+ virginica
+
+
+
+
+ sepal_length
+ 5.006
+ 5.936
+ 6.588
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/chapters/module-4/07-numpy-continued.html b/chapters/module-4/07-numpy-continued.html
index e2141b6..c5be5d4 100644
--- a/chapters/module-4/07-numpy-continued.html
+++ b/chapters/module-4/07-numpy-continued.html
@@ -208,7 +208,8 @@
diff --git a/chapters/module-4/Untitled.html b/chapters/module-4/Untitled.html
index 66d0a21..c6e16b2 100644
--- a/chapters/module-4/Untitled.html
+++ b/chapters/module-4/Untitled.html
@@ -208,7 +208,8 @@
diff --git a/genindex.html b/genindex.html
index 1de5a9f..dc75e9f 100644
--- a/genindex.html
+++ b/genindex.html
@@ -208,6 +208,7 @@
- NumPy (Part I)
- NumPy (Part II)
- Introduction to Pandas
+- Pandas: Data Exploration
diff --git a/index.html b/index.html
index ff1cd20..d9885f1 100644
--- a/index.html
+++ b/index.html
@@ -58,6 +58,8 @@
+
+
@@ -210,6 +212,7 @@
- NumPy (Part I)
- NumPy (Part II)
- Introduction to Pandas
+- Pandas: Data Exploration
diff --git a/objects.inv b/objects.inv
index 8747087..6939f6f 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/reports/chapters/module-4/044-PandasII-Exploration_and_Manipulation.err.log b/reports/chapters/module-4/044-PandasII-Exploration_and_Manipulation-Copy1.err.log
similarity index 70%
rename from reports/chapters/module-4/044-PandasII-Exploration_and_Manipulation.err.log
rename to reports/chapters/module-4/044-PandasII-Exploration_and_Manipulation-Copy1.err.log
index 7edfc45..b0f92a5 100644
--- a/reports/chapters/module-4/044-PandasII-Exploration_and_Manipulation.err.log
+++ b/reports/chapters/module-4/044-PandasII-Exploration_and_Manipulation-Copy1.err.log
@@ -13,14 +13,13 @@ Traceback (most recent call last):
raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
------------------
-df['y'], type(df['y'])
+iris_df.'sepal_volume_3' = iris_df.sepal_length + iris_df.sepal_width
------------------
-[0;31m---------------------------------------------------------------------------[0m
-[0;31mNameError[0m Traceback (most recent call last)
-Cell [0;32mIn[45], line 1[0m
-[0;32m----> 1[0m df[[38;5;124m'[39m[38;5;124my[39m[38;5;124m'[39m], [38;5;28mtype[39m(df[[38;5;124m'[39m[38;5;124my[39m[38;5;124m'[39m])
+[0;36m Cell [0;32mIn[28], line 1[0;36m[0m
+[0;31m iris_df.'sepal_volume_3' = iris_df.sepal_length + iris_df.sepal_width[0m
+[0m ^[0m
+[0;31mSyntaxError[0m[0;31m:[0m invalid syntax
-[0;31mNameError[0m: name 'df' is not defined
diff --git a/reports/chapters/module-4/044-PandasII-exploration.err.log b/reports/chapters/module-4/044-PandasII-exploration.err.log
new file mode 100644
index 0000000..15c88b3
--- /dev/null
+++ b/reports/chapters/module-4/044-PandasII-exploration.err.log
@@ -0,0 +1,99 @@
+Traceback (most recent call last):
+ File "/home/javi/anaconda3/lib/python3.11/site-packages/myst_nb/core/execute/inline.py", line 120, in code_cell_outputs
+ self._client.execute_cell(
+ File "/home/javi/anaconda3/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped
+ return loop.run_until_complete(inner)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/javi/anaconda3/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete
+ return future.result()
+ ^^^^^^^^^^^^^^^
+ File "/home/javi/anaconda3/lib/python3.11/site-packages/nbclient/client.py", line 1058, in async_execute_cell
+ await self._check_raise_for_error(cell, cell_index, exec_reply)
+ File "/home/javi/anaconda3/lib/python3.11/site-packages/nbclient/client.py", line 914, in _check_raise_for_error
+ raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
+nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
+------------------
+# But this will give an error
+iris_copy.loc[1:3,:]
+------------------
+
+
+[0;31m---------------------------------------------------------------------------[0m
+[0;31mTypeError[0m Traceback (most recent call last)
+Cell [0;32mIn[41], line 2[0m
+[1;32m 1[0m [38;5;66;03m# But this will give an error[39;00m
+[0;32m----> 2[0m iris_copy[38;5;241m.[39mloc[[38;5;241m1[39m:[38;5;241m3[39m,:]
+
+File [0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1067[0m, in [0;36m_LocationIndexer.__getitem__[0;34m(self, key)[0m
+[1;32m 1065[0m [38;5;28;01mif[39;00m [38;5;28mself[39m[38;5;241m.[39m_is_scalar_access(key):
+[1;32m 1066[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39mobj[38;5;241m.[39m_get_value([38;5;241m*[39mkey, takeable[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39m_takeable)
+[0;32m-> 1067[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39m_getitem_tuple(key)
+[1;32m 1068[0m [38;5;28;01melse[39;00m:
+[1;32m 1069[0m [38;5;66;03m# we by definition only have the 0th axis[39;00m
+[1;32m 1070[0m axis [38;5;241m=[39m [38;5;28mself[39m[38;5;241m.[39maxis [38;5;129;01mor[39;00m [38;5;241m0[39m
+
+File [0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1256[0m, in [0;36m_LocIndexer._getitem_tuple[0;34m(self, tup)[0m
+[1;32m 1253[0m [38;5;28;01mif[39;00m [38;5;28mself[39m[38;5;241m.[39m_multi_take_opportunity(tup):
+[1;32m 1254[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39m_multi_take(tup)
+[0;32m-> 1256[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39m_getitem_tuple_same_dim(tup)
+
+File [0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:924[0m, in [0;36m_LocationIndexer._getitem_tuple_same_dim[0;34m(self, tup)[0m
+[1;32m 921[0m [38;5;28;01mif[39;00m com[38;5;241m.[39mis_null_slice(key):
+[1;32m 922[0m [38;5;28;01mcontinue[39;00m
+[0;32m--> 924[0m retval [38;5;241m=[39m [38;5;28mgetattr[39m(retval, [38;5;28mself[39m[38;5;241m.[39mname)[38;5;241m.[39m_getitem_axis(key, axis[38;5;241m=[39mi)
+[1;32m 925[0m [38;5;66;03m# We should never have retval.ndim < self.ndim, as that should[39;00m
+[1;32m 926[0m [38;5;66;03m# be handled by the _getitem_lowerdim call above.[39;00m
+[1;32m 927[0m [38;5;28;01massert[39;00m retval[38;5;241m.[39mndim [38;5;241m==[39m [38;5;28mself[39m[38;5;241m.[39mndim
+
+File [0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1290[0m, in [0;36m_LocIndexer._getitem_axis[0;34m(self, key, axis)[0m
+[1;32m 1288[0m [38;5;28;01mif[39;00m [38;5;28misinstance[39m(key, [38;5;28mslice[39m):
+[1;32m 1289[0m [38;5;28mself[39m[38;5;241m.[39m_validate_key(key, axis)
+[0;32m-> 1290[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39m_get_slice_axis(key, axis[38;5;241m=[39maxis)
+[1;32m 1291[0m [38;5;28;01melif[39;00m com[38;5;241m.[39mis_bool_indexer(key):
+[1;32m 1292[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39m_getbool_axis(key, axis[38;5;241m=[39maxis)
+
+File [0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1324[0m, in [0;36m_LocIndexer._get_slice_axis[0;34m(self, slice_obj, axis)[0m
+[1;32m 1321[0m [38;5;28;01mreturn[39;00m obj[38;5;241m.[39mcopy(deep[38;5;241m=[39m[38;5;28;01mFalse[39;00m)
+[1;32m 1323[0m labels [38;5;241m=[39m obj[38;5;241m.[39m_get_axis(axis)
+[0;32m-> 1324[0m indexer [38;5;241m=[39m labels[38;5;241m.[39mslice_indexer(slice_obj[38;5;241m.[39mstart, slice_obj[38;5;241m.[39mstop, slice_obj[38;5;241m.[39mstep)
+[1;32m 1326[0m [38;5;28;01mif[39;00m [38;5;28misinstance[39m(indexer, [38;5;28mslice[39m):
+[1;32m 1327[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39mobj[38;5;241m.[39m_slice(indexer, axis[38;5;241m=[39maxis)
+
+File [0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6559[0m, in [0;36mIndex.slice_indexer[0;34m(self, start, end, step, kind)[0m
+[1;32m 6516[0m [38;5;250m[39m[38;5;124;03m"""[39;00m
+[1;32m 6517[0m [38;5;124;03mCompute the slice indexer for input labels and step.[39;00m
+[1;32m 6518[0m
+[0;32m (...)[0m
+[1;32m 6555[0m [38;5;124;03mslice(1, 3, None)[39;00m
+[1;32m 6556[0m [38;5;124;03m"""[39;00m
+[1;32m 6557[0m [38;5;28mself[39m[38;5;241m.[39m_deprecated_arg(kind, [38;5;124m"[39m[38;5;124mkind[39m[38;5;124m"[39m, [38;5;124m"[39m[38;5;124mslice_indexer[39m[38;5;124m"[39m)
+[0;32m-> 6559[0m start_slice, end_slice [38;5;241m=[39m [38;5;28mself[39m[38;5;241m.[39mslice_locs(start, end, step[38;5;241m=[39mstep)
+[1;32m 6561[0m [38;5;66;03m# return a slice[39;00m
+[1;32m 6562[0m [38;5;28;01mif[39;00m [38;5;129;01mnot[39;00m is_scalar(start_slice):
+
+File [0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6767[0m, in [0;36mIndex.slice_locs[0;34m(self, start, end, step, kind)[0m
+[1;32m 6765[0m start_slice [38;5;241m=[39m [38;5;28;01mNone[39;00m
+[1;32m 6766[0m [38;5;28;01mif[39;00m start [38;5;129;01mis[39;00m [38;5;129;01mnot[39;00m [38;5;28;01mNone[39;00m:
+[0;32m-> 6767[0m start_slice [38;5;241m=[39m [38;5;28mself[39m[38;5;241m.[39mget_slice_bound(start, [38;5;124m"[39m[38;5;124mleft[39m[38;5;124m"[39m)
+[1;32m 6768[0m [38;5;28;01mif[39;00m start_slice [38;5;129;01mis[39;00m [38;5;28;01mNone[39;00m:
+[1;32m 6769[0m start_slice [38;5;241m=[39m [38;5;241m0[39m
+
+File [0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6676[0m, in [0;36mIndex.get_slice_bound[0;34m(self, label, side, kind)[0m
+[1;32m 6672[0m original_label [38;5;241m=[39m label
+[1;32m 6674[0m [38;5;66;03m# For datetime indices label may be a string that has to be converted[39;00m
+[1;32m 6675[0m [38;5;66;03m# to datetime boundary according to its resolution.[39;00m
+[0;32m-> 6676[0m label [38;5;241m=[39m [38;5;28mself[39m[38;5;241m.[39m_maybe_cast_slice_bound(label, side)
+[1;32m 6678[0m [38;5;66;03m# we need to look up the label[39;00m
+[1;32m 6679[0m [38;5;28;01mtry[39;00m:
+
+File [0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6623[0m, in [0;36mIndex._maybe_cast_slice_bound[0;34m(self, label, side, kind)[0m
+[1;32m 6618[0m [38;5;66;03m# We are a plain index here (sub-class override this method if they[39;00m
+[1;32m 6619[0m [38;5;66;03m# wish to have special treatment for floats/ints, e.g. Float64Index and[39;00m
+[1;32m 6620[0m [38;5;66;03m# datetimelike Indexes[39;00m
+[1;32m 6621[0m [38;5;66;03m# reject them, if index does not contain label[39;00m
+[1;32m 6622[0m [38;5;28;01mif[39;00m (is_float(label) [38;5;129;01mor[39;00m is_integer(label)) [38;5;129;01mand[39;00m label [38;5;129;01mnot[39;00m [38;5;129;01min[39;00m [38;5;28mself[39m:
+[0;32m-> 6623[0m [38;5;28;01mraise[39;00m [38;5;28mself[39m[38;5;241m.[39m_invalid_indexer([38;5;124m"[39m[38;5;124mslice[39m[38;5;124m"[39m, label)
+[1;32m 6625[0m [38;5;28;01mreturn[39;00m label
+
+[0;31mTypeError[0m: cannot do slice indexing on Index with these indexers [1] of type int
+
diff --git a/reports/chapters/module-4/045-PandasIII-manipulation.err.log b/reports/chapters/module-4/045-PandasIII-manipulation.err.log
new file mode 100644
index 0000000..5212c06
--- /dev/null
+++ b/reports/chapters/module-4/045-PandasIII-manipulation.err.log
@@ -0,0 +1,25 @@
+Traceback (most recent call last):
+ File "/home/javi/anaconda3/lib/python3.11/site-packages/myst_nb/core/execute/inline.py", line 120, in code_cell_outputs
+ self._client.execute_cell(
+ File "/home/javi/anaconda3/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped
+ return loop.run_until_complete(inner)
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ File "/home/javi/anaconda3/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete
+ return future.result()
+ ^^^^^^^^^^^^^^^
+ File "/home/javi/anaconda3/lib/python3.11/site-packages/nbclient/client.py", line 1058, in async_execute_cell
+ await self._check_raise_for_error(cell, cell_index, exec_reply)
+ File "/home/javi/anaconda3/lib/python3.11/site-packages/nbclient/client.py", line 914, in _check_raise_for_error
+ raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
+nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
+------------------
+iris_df.'sepal_volume_3' = iris_df.sepal_length + iris_df.sepal_width
+------------------
+
+
+[0;36m Cell [0;32mIn[12], line 1[0;36m[0m
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diff --git a/search.html b/search.html
index 2a35b9b..2491979 100644
--- a/search.html
+++ b/search.html
@@ -210,6 +210,7 @@
- NumPy (Part I)
- NumPy (Part II)
- Introduction to Pandas
+- Pandas: Data Exploration
diff --git a/searchindex.js b/searchindex.js
index ba9bd6c..3484bcb 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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