1 |
Introduction to Data Science |
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2 |
Introduction to Pandas in Python |
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3 |
How to Install Python Pandas on Windows and Linux
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4 |
How To Use Jupyter Notebook: An Ultimate Guide |
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5 |
Python → Pandas DataFrame |
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6 |
Creating a Pandas DataFrame |
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Python → Pandas Series |
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Creating a Pandas Series |
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9 |
View the top rows of the frame |
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10 |
View the bottom rows of the frame |
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11 |
View basic statistical details |
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12 |
Convert the pandas DataFrame to numpy Array |
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Convert the pandas Series to numpy Array |
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14 |
()Convert series or dataframe object to Numpy-array using .as_matrix |
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15 |
Dealing with Rows and Columns in Pandas DataFrame |
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How to select multiple columns in a pandas dataframe |
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17 |
Python → Pandas Extracting rows using .loc[] |
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Python → Extracting rows using Pandas .iloc[] |
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19 |
Indexing and Selecting Data with Pandas |
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20 |
Boolean Indexing in Pandas |
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21 |
[ ]Label and Integer based slicing technique using DataFrame.ix |
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22 |
Adding new column to existing DataFrame in Pandas |
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23 |
Python → Delete rows/columns from DataFrame |
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24 |
Truncate a DataFrame before and after some index value |
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Truncate a Series before and after some index value |
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Iterating over rows and columns in Pandas DataFrame |
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Working with Missing Data in Pandas |
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Sorts a data frame in Pandas → Set-1 |
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29 |
Sorts a data frame in Pandas → Set-2 |
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30 |
Pandas GroupBy |
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31 |
Grouping Rows in pandas |
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32 |
Combining multiple columns in Pandas groupby with dictionary |
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Python → Pandas Merging, Joining, and Concatenating |
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34 |
Concatenate Strings |
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35 |
Append rows to Dataframe |
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Concatenate two or more series |
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Append a single or a collection of indices |
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Combine two series into one |
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Add a row at top in pandas DataFrame |
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Join all elements in list present in a series |
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41 |
Join two text columns into a single column in Pandas |
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42 |
Python → Working with date and time using Pandas |
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43 |
Timestamp using Pandas |
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44 |
Current Time using Pandas |
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45 |
Convert timestamp to ISO Format |
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46 |
Get datetime object using Pandas |
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Replace the member values of the given Timestamp |
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48 |
Convert string Date time into Python Date time object using Pandas |
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49 |
Get a fixed frequency DatetimeIndex using Pandas |
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50 |
Python → Pandas Working With Text Data |
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51 |
Convert String into lower, upper or camel case |
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52 |
Replace Text Value |
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53 |
()Replace Text Value using series.replace |
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54 |
Removing Whitespaces |
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55 |
Move dates forward a given number of valid dates using Pandas |
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56 |
Read csv using pandas |
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57 |
Saving a Pandas Dataframe as a CSV |
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58 |
Loading Excel spreadsheet as pandas DataFrame |
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59 |
Creating a dataframe using Excel files |
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60 |
Working with Pandas and XlsxWriter → Set – 1 |
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Working with Pandas and XlsxWriter → Set – 2 |
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Working with Pandas and XlsxWriter → Set – 3 |
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63 |
Apply a function on the possible series |
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Apply function to every row in a Pandas DataFrame |
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Apply a function on each element of the series |
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Aggregation data across one or more column |
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67 |
Mean of the values for the requested axis |
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Mean of the underlying data in the Series |
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Mean absolute deviation of the values for the requested axis |
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Mean absolute deviation of the values for the Series |
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71 |
Unbiased standard error of the mean |
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72 |
Find the Series containing counts of unique values |
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73 |
()Find the Series containing counts of unique values using Index.value_counts |
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74 |
Pandas Built-in Data Visualization |
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Data analysis and Visualization with Python → Set 1 |
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Data analysis and Visualization with Python → Set 2 |
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77 |
Box plot visualization with Pandas and Seaborn |
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78 |
How to Do a vLookup in Python using pandas |
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Convert CSV to HTML Table in Python |
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80 |
KDE Plot Visualization with Pandas and Seaborn |
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81 |
Analyzing selling price of used cars using Python |
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82 |
Add CSS to the Jupyter Notebook using Pandas |
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زمان تخمینی مورد نیاز برای این دوره: 60 ساعت
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