Pandas
by
Reuven M. Lerner
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Section 4: Advanced series functionality
Section 4: Advanced series functionality
Pandas, part 4 -- advanced series.ipynb
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25 size and count
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26 median and quantiles
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27 describe
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28 describe with non-numeric data
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29 head and tail
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30 value_counts
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31 duplicated
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32 replace
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33 Sorting
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34 apply
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35 Exercises 3
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exercise-3.txt
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36 Exercise solutions 3
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Table of contents
Pandas
by
Reuven M. Lerner
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Section 1: Introduction
Section 1: Introduction
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Pandas, part 1 -- introduction.ipynb
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Pandas, part 1 -- introduction.ipynb
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1 Introduction
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1 Introduction
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2 what-is-pandas.mov
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2 what-is-pandas.mov
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3 Installing Pandas
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3 Installing Pandas
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4 Loading Pandas into Jupyter
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4 Loading Pandas into Jupyter
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Section 2: Series
Section 2: Series
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Pandas, part 2 -- series.ipynb
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Pandas, part 2 -- series.ipynb
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5 Creating a series
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5 Creating a series
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6 Creating a series with NumPy
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6 Creating a series with NumPy
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7 Setting and retrieving with indexes
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7 Setting and retrieving with indexes
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8 Retrieving with loc and iloc
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8 Retrieving with loc and iloc
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9 Setting the index
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9 Setting the index
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10 Non-unique indexes
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10 Non-unique indexes
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10a Fancy indexing
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10a Fancy indexing
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11 Basic methods
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11 Basic methods
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12 Operations by index
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12 Operations by index
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13 Broadcasting operators
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13 Broadcasting operators
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14 Boolean indexing
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14 Boolean indexing
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15 Exercises 1
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15 Exercises 1
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exercise-1.txt
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exercise-1.txt
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16 Exercise solutions 1
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16 Exercise solutions 1
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Section 3: dtypes and NaN
Section 3: dtypes and NaN
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Pandas, part 3 -- dtypes and NaN.ipynb
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Pandas, part 3 -- dtypes and NaN.ipynb
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17 dtypes
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17 dtypes
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18 assigning to dtypes
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18 assigning to dtypes
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19 Using astype
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19 Using astype
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20 NaN
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20 NaN
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20a Skipping NaN
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20a Skipping NaN
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21 dropna and fillna
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21 dropna and fillna
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22 fill_value
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22 fill_value
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23 Exercises 2
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23 Exercises 2
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exercise-2.txt
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exercise-2.txt
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24 Exercise solutions 2
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24 Exercise solutions 2
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Section 4: Advanced series functionality
Section 4: Advanced series functionality
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Pandas, part 4 -- advanced series.ipynb
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Pandas, part 4 -- advanced series.ipynb
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25 size and count
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25 size and count
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26 median and quantiles
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26 median and quantiles
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27 describe
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27 describe
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28 describe with non-numeric data
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28 describe with non-numeric data
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29 head and tail
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29 head and tail
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30 value_counts
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30 value_counts
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31 duplicated
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31 duplicated
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32 replace
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32 replace
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33 Sorting
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33 Sorting
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34 apply
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34 apply
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35 Exercises 3
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35 Exercises 3
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exercise-3.txt
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exercise-3.txt
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36 Exercise solutions 3
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36 Exercise solutions 3
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Section 5: Strings
Section 5: Strings
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Pandas, part 5 -- Strings.ipynb
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Pandas, part 5 -- Strings.ipynb
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37 Strings in Pandas vs NumPy
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37 Strings in Pandas vs NumPy
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38 String methods and the "str" object
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38 String methods and the "str" object
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39 Finding numbers
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39 Finding numbers
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40 startswith and endswith
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40 startswith and endswith
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41 [] and strings
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41 [] and strings
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42 str.contains
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42 str.contains
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43 find and index
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43 find and index
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44 Modifying data
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44 Modifying data
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45 Splitting and reusing str
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45 Splitting and reusing str
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46 Exercises 4
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46 Exercises 4
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exercise-4.txt
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exercise-4.txt
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47 Exercise 4 solutions
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47 Exercise 4 solutions
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Section 6: Plotting series
Section 6: Plotting series
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Pandas, part 6 -- plotting.ipynb
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Pandas, part 6 -- plotting.ipynb
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48 Simple plots with matplotlib
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48 Simple plots with matplotlib
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49 More sophisticated plotting with Matplotlib
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49 More sophisticated plotting with Matplotlib
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50 Line plots via pandas
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50 Line plots via pandas
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51 Bar plots with pandas
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51 Bar plots with pandas
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52 Histograms
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52 Histograms
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53 Pie plots
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53 Pie plots
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54 Box plots
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54 Box plots
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55 Exercises 5
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55 Exercises 5
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exercise-5.txt
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exercise-5.txt
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56 Exercise 5 solutions
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56 Exercise 5 solutions
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Section 7: Data frames
Section 7: Data frames
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Pandas, part 7 -- data frames.ipynb
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Pandas, part 7 -- data frames.ipynb
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57 Data frame introduction
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57 Data frame introduction
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58 Index and columns -- simple retrievals
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58 Index and columns -- simple retrievals
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58a Dot syntax for column retrieval
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58a Dot syntax for column retrieval
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59 Setting the index and columns
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59 Setting the index and columns
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60 Retrieving an individual value
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60 Retrieving an individual value
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61 Creating data frames from NumPy arrays
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61 Creating data frames from NumPy arrays
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62 Creating data frames from a list of dicts
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62 Creating data frames from a list of dicts
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63 Creating data frames from a dict of lists, arrays, or series
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63 Creating data frames from a dict of lists, arrays, or series
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64 Methods on columns
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64 Methods on columns
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65 Methods on an entire data frame
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65 Methods on an entire data frame
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66 Retrieving multiple columns
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66 Retrieving multiple columns
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67 Retrieving multiple rows
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67 Retrieving multiple rows
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68 Updating values in a data frame
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68 Updating values in a data frame
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68a Using "describe" on data frames
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68a Using "describe" on data frames
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68b Updating a column
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68b Updating a column
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68c Adding columns
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68c Adding columns
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68d Updating values in rows and adding rows
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68d Updating values in rows and adding rows
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68e Dropping one or more rows
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68e Dropping one or more rows
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68f Dropping one or more columns
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68f Dropping one or more columns
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69 Exercises 6
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69 Exercises 6
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exercise-6.txt
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exercise-6.txt
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70 Exercise solutions 6
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70 Exercise solutions 6
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Section 8: Boolean indexes and data frames
Section 8: Boolean indexes and data frames
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Pandas, part 8 -- boolean indexes and data frames.ipynb
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Pandas, part 8 -- boolean indexes and data frames.ipynb
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71 Boolean indexes on a column
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71 Boolean indexes on a column
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72 Applying boolean indexes to other columns
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72 Applying boolean indexes to other columns
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73 Complex queries across columns
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73 Complex queries across columns
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74 Applying a boolean index to an entire data frame
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74 Applying a boolean index to an entire data frame
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75 Assigning to data frames -- recap
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75 Assigning to data frames -- recap
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76 Assigning to multiple rows and columns with loc
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76 Assigning to multiple rows and columns with loc
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77 Assigning to a column based on a boolean index
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77 Assigning to a column based on a boolean index
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78 Chained assignment -- what it is, and how to avoid it
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78 Chained assignment -- what it is, and how to avoid it
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79 Data frame assignment example
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79 Data frame assignment example
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80 Assigning a scalar value to a data frame, based on a condition
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80 Assigning a scalar value to a data frame, based on a condition
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81 Assigning a vector value to a data frame, based on a condition
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81 Assigning a vector value to a data frame, based on a condition
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82 Using df.replace to replace values across a data frame
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82 Using df.replace to replace values across a data frame
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82a Using isna, dropna, and fillna with data frames
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82a Using isna, dropna, and fillna with data frames
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83 Using mask and where
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83 Using mask and where
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84 Using clip
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84 Using clip
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85 Exercises 7
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85 Exercises 7
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exercise-7.txt
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exercise-7.txt
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86 Exercise solutions 7
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86 Exercise solutions 7
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Section 9: Pandas and I/O — reading and writing files
Section 9: Pandas and I/O — reading and writing files
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Pandas, part 9 -- input and output.ipynb
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Pandas, part 9 -- input and output.ipynb
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87 Pandas and IO -- and saving to the clipboard
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87 Pandas and IO -- and saving to the clipboard
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88 Saving to CSV
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88 Saving to CSV
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89 Changing the CSV separator
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89 Changing the CSV separator
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90 NaN representation
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90 NaN representation
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91 Choosing output columns
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91 Choosing output columns
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92 Writing row and column names
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92 Writing row and column names
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93 Saving with compression
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93 Saving with compression
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94 Reading CSV files
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94 Reading CSV files
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95 Choosing and ignoring header rows
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95 Choosing and ignoring header rows
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96 Naming columns
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96 Naming columns
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97 Choosing columns
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97 Choosing columns
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98 Choosing + naming
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98 Choosing + naming
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99 Reading NaN values
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99 Reading NaN values
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100 dtype hints when reading CSV
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100 dtype hints when reading CSV
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101 Reading from the network
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101 Reading from the network
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102 Exercises 8
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102 Exercises 8
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exercise-8.txt
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exercise-8.txt
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103 Exercise 8 solutions
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103 Exercise 8 solutions
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104 Excel files
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104 Excel files
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105 JSON files
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105 JSON files
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106 SQL databases
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106 SQL databases
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Section 10: Data analysis with Pandas
Section 10: Data analysis with Pandas
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Pandas, part 10 -- analysis of real-world data.ipynb
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Pandas, part 10 -- analysis of real-world data.ipynb
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flight-delays.zip
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flight-delays.zip
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taxi.csv
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taxi.csv
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107 Analysis of taxi data
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107 Analysis of taxi data
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108 Taxi data part 2
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108 Taxi data part 2
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109 Taxi data part 3
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109 Taxi data part 3
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110 Taxi data part 4
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110 Taxi data part 4
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111 Taxi data part 5
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111 Taxi data part 5
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112 Taxi data part 6
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112 Taxi data part 6
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113 Exercises 9
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113 Exercises 9
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114 Exercise 9 solutions
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114 Exercise 9 solutions
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Section 11: Memory management and categories
Section 11: Memory management and categories
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Pandas, part 11 -- Memory management and categories.ipynb
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Pandas, part 11 -- Memory management and categories.ipynb
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115 Data frames and memory usage
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115 Data frames and memory usage
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116 Memory usage in series and data frames
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116 Memory usage in series and data frames
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117 Categories.mp4
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117 Categories.mp4
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118 Setting dtypes upon load.mp4
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118 Setting dtypes upon load.mp4
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119 Predefining categories
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119 Predefining categories
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120 Avoiding low-memory warnings.mp4
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120 Avoiding low-memory warnings.mp4
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exercise-10.txt
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exercise-10.txt
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airlines.dat
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airlines.dat
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121 Exercises 10.mp4
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121 Exercises 10.mp4
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122 Exercise solutions 10.mp4
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122 Exercise solutions 10.mp4
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Section 12: Indexes and multi-indexes
Section 12: Indexes and multi-indexes
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Pandas, part 12 -- indexes.ipynb
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Pandas, part 12 -- indexes.ipynb
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123 set_index and reset_index.mp4
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123 set_index and reset_index.mp4
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124 multi-indexes on series.mp4
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124 multi-indexes on series.mp4
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125 multi-indexes on data frames.mp4
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125 multi-indexes on data frames.mp4
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126 stack and unstack.mp4
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126 stack and unstack.mp4
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127 swaplevel.mp4
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127 swaplevel.mp4
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128 Exercises 11.mp4
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128 Exercises 11.mp4
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exercise-11.txt
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exercise-11.txt
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129 Exercise solutions 11.mp4
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129 Exercise solutions 11.mp4
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college.csv
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college.csv
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Section 13: Sorting, merging, and grouping
Section 13: Sorting, merging, and grouping
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Pandas, part 13 -- sorting, joining, merging, and grouping.ipynb
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Pandas, part 13 -- sorting, joining, merging, and grouping.ipynb
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reuven_travel.csv
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reuven_travel.csv
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oecd_locations.csv
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oecd_locations.csv
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phonecodes.json
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phonecodes.json
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oecd_tourist_spending.csv
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oecd_tourist_spending.csv
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weather.zip
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weather.zip
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130 sort_index.mp4
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130 sort_index.mp4
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131 sort_values.mp4
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131 sort_values.mp4
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132 concatenating data frames.mp4
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132 concatenating data frames.mp4
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133 concatenating different data frames.mp4
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133 concatenating different data frames.mp4
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134 Inner and outer joins.mp4
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134 Inner and outer joins.mp4
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135 merging -- inner, left, right, and outer.mp4
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135 merging -- inner, left, right, and outer.mp4
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136 groupby.mp4
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136 groupby.mp4
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137 pivot tables.mp4
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137 pivot tables.mp4
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138 Exercises 12.mp4
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138 Exercises 12.mp4
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exercise-12.txt
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exercise-12.txt
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139 Exercise solutions 12.mp4
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139 Exercise solutions 12.mp4
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Section 14: Plotting data frames
Section 14: Plotting data frames
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140 Plotting data frames.mp4
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140 Plotting data frames.mp4
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141 Bar plots.mp4
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141 Bar plots.mp4
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142 Stacked bar plots.mp4
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142 Stacked bar plots.mp4
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143 Histograms.mp4
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143 Histograms.mp4
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144 Pie plots.mp4
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144 Pie plots.mp4
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145 Box plots.mp4
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145 Box plots.mp4
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146 Scatter plots.mp4
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146 Scatter plots.mp4
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147 Scatter plots and colormaps.mp4
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147 Scatter plots and colormaps.mp4
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148 Scatter matrix.mp4
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148 Scatter matrix.mp4
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149 Exercises 13.mp4
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149 Exercises 13.mp4
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150 Exercise 13 solutions.mp4
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150 Exercise 13 solutions.mp4
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exercise-13.txt
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exercise-13.txt
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Pandas, part 14 -- plotting.ipynb
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Pandas, part 14 -- plotting.ipynb
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Conclusion
Conclusion
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151 Conclusion
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151 Conclusion