种子简介

种子名称： Data Analysis with Pandas and Python
文件类型： 视频
文件数目： 173个文件
文件大小： 2.33 GB
收录时间： 2018-7-27 16:09
已经下载： 3次
资源热度： 113
最近下载： 2024-6-24 13:13

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:b87385f0d8f1fc9fa342f8b4e3ed2473465dfe88&dn=Data Analysis with Pandas and Python 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

Data Analysis with Pandas and Python.torrent

01 Installation and Setup/006 Mac OS - Update Anaconda Libraries.mp435.27MB

01 Installation and Setup/001 Introduction to the Course.mp434MB

01 Installation and Setup/003 Mac OS - Download the Anaconda Distribution.mp47.92MB

01 Installation and Setup/004 Mac OS - Install Anaconda Distribution.mp422.76MB

01 Installation and Setup/005 Mac OS - Access the Terminal.mp48.84MB

01 Installation and Setup/007 Mac OS - Unpack Course Materials The Startdown and Shutdown Process.mp422.15MB

01 Installation and Setup/008 Windows - Download the Anaconda Distribution.mp49.27MB

01 Installation and Setup/009 Windows - Install Anaconda Distribution.mp418.58MB

01 Installation and Setup/010 Windows - Access the Command Prompt and Update Anaconda Libraries.mp422.5MB

01 Installation and Setup/011 Windows - Unpack Course Materials The Startdown and Shutdown Process.mp417.72MB

01 Installation and Setup/012 Intro to the Jupyter Notebook Interface.mp49.31MB

01 Installation and Setup/013 Cell Types and Cell Modes.mp411.67MB

01 Installation and Setup/014 Code Cell Execution.mp48.22MB

01 Installation and Setup/015 Popular Keyboard Shortcuts.mp46.26MB

01 Installation and Setup/016 Import Libraries into Jupyter Notebook.mp411.53MB

01 Installation and Setup/017 Python Crash Course Part 1 - Data Types and Variables.mp411.98MB

01 Installation and Setup/018 Python Crash Course Part 2 - Lists.mp49MB

01 Installation and Setup/019 Python Crash Course Part 3 - Dictionaries.mp47.2MB

01 Installation and Setup/020 Python Crash Course Part 4 - Operators.mp47.87MB

01 Installation and Setup/021 Python Crash Course Part 5 - Functions.mp410.12MB

02 Series/022 Create Jupyter Notebook for the Series Module.mp43.8MB

02 Series/023 Create A Series Object from a Python List.mp418.12MB

02 Series/024 Create A Series Object from a Python Dictionary.mp45.19MB

02 Series/025 Intro to Attributes.mp412.85MB

02 Series/026 Intro to Methods.mp47.91MB

02 Series/027 Parameters and Arguments.mp418.28MB

02 Series/028 Import Series with the .read_csv() Method.mp421.14MB

02 Series/029 The .head() and .tail() Methods.mp46.47MB

02 Series/030 Python Built-In Functions.mp49.87MB

02 Series/031 More Series Attributes.mp411.66MB

02 Series/032 The .sort_values() Method.mp410.83MB

02 Series/033 The inplace Parameter.mp49.39MB

02 Series/034 The .sort_index() Method.mp48.57MB

02 Series/035 Pythons in Keyword.mp47.3MB

02 Series/036 Extract Series Values by Index Position.mp48.9MB

02 Series/037 Extract Series Values by Index Label.mp413.73MB

02 Series/038 The .get() Method on a Series.mp49.56MB

02 Series/039 Math Methods on Series Objects.mp410.16MB

02 Series/040 The .idxmax() and .idxmin() Methods.mp45.75MB

02 Series/041 The .value_counts() Method.mp46.73MB

02 Series/042 The .apply() Method.mp412.31MB

02 Series/043 The .map() Method.mp413.09MB

03 DataFrames I/044 Intro to DataFrames I Module.mp417.63MB

03 DataFrames I/045 Shared Methods and Attributes between Series and DataFrames.mp415.62MB

03 DataFrames I/046 Differences between Shared Methods.mp413.1MB

03 DataFrames I/047 Select One Column from a DataFrame.mp414.87MB

03 DataFrames I/048 Select Two or More Columns from a DataFrame.mp49.93MB

03 DataFrames I/049 Add New Column to DataFrame.mp417.23MB

03 DataFrames I/050 Broadcasting Operations.mp418.23MB

03 DataFrames I/051 A Review of the .value_counts() Method.mp48.42MB

03 DataFrames I/052 Drop Rows with Null Values.mp419.2MB

03 DataFrames I/053 Fill in Null Values with the .fillna() Method.mp410.75MB

03 DataFrames I/054 The .astype() Method.mp423.86MB

03 DataFrames I/055 Sort a DataFrame with the .sort_values() Method Part I.mp413.27MB

03 DataFrames I/056 Sort a DataFrame with the .sort_values() Method Part II.mp48.83MB

03 DataFrames I/057 Sort DataFrame with the .sort_index() Method.mp46.56MB

03 DataFrames I/058 Rank Values with the .rank() Method.mp413.16MB

04 DataFrames II/059 This Modules Dataset Memory Optimization.mp424.44MB

04 DataFrames II/060 Filter a DataFrame Based on A Condition.mp427.4MB

04 DataFrames II/061 Filter with More than One Condition (AND - ).mp49.29MB

04 DataFrames II/062 Filter with More than One Condition (OR - ).mp416.75MB

04 DataFrames II/063 The .isin() Method.mp412.53MB

04 DataFrames II/064 The .isnull() and .notnull() Methods.mp412.26MB

04 DataFrames II/065 The .between() Method.mp416.76MB

04 DataFrames II/066 The .duplicated() Method.mp419.56MB

04 DataFrames II/067 The .drop_duplicates() Method.mp417.55MB

04 DataFrames II/068 The .unique() and .nunique() Methods.mp48.19MB

05 DataFrames III/069 Intro to the DataFrames III Module Import Dataset.mp47.67MB

05 DataFrames III/070 The .set_index() and .reset_index() Methods.mp413.19MB

05 DataFrames III/071 Retrieve Rows by Index Label with .loc.mp425.87MB

05 DataFrames III/072 Retrieve Rows by Index Position with .iloc.mp413.3MB

05 DataFrames III/073 The Catch-All .ix Method.mp418.56MB

05 DataFrames III/074 Second Arguments to .loc .iloc and .ix Methods.mp412.36MB

05 DataFrames III/075 Set New Values for a Specific Cell or Row.mp48.89MB

05 DataFrames III/076 Set Multiple Values in DataFrame.mp420.54MB

05 DataFrames III/077 Rename Index Labels or Columns in a DataFrame.mp413.39MB

05 DataFrames III/078 Delete Rows or Columns from a DataFrame.mp416.21MB

05 DataFrames III/079 Create Random Sample with the .sample() Method.mp49.33MB

05 DataFrames III/080 The .nsmallest() and .nlargest() Methods.mp412.07MB

05 DataFrames III/081 Filtering with the .where() Method.mp413.56MB

05 DataFrames III/082 The .query() Method.mp419.92MB

05 DataFrames III/083 A Review of the .apply() Method on Single Columns.mp411.75MB

05 DataFrames III/084 The .apply() Method with Row Values.mp413.41MB

05 DataFrames III/085 The .copy() Method.mp415.44MB

06 Working with Text Data/086 Intro to the Working with Text Data Module.mp413.86MB

06 Working with Text Data/087 Common String Methods - lower upper title and len.mp414.88MB

06 Working with Text Data/088 The .str.replace() Method.mp416MB

06 Working with Text Data/089 Filtering with String Methods.mp415.54MB

06 Working with Text Data/090 More String Methods - strip lstrip and rstrip.mp49.54MB

06 Working with Text Data/091 String Methods on Index and Columns.mp411.12MB

06 Working with Text Data/092 Split Strings by Characters with .str.split() Method.mp417.52MB

06 Working with Text Data/093 More Practice with Splits.mp411.92MB

06 Working with Text Data/094 The expand and n Parameters of the .str.split() Method.mp415.3MB

07 MultiIndex/095 Intro to the MultiIndex Module.mp48.32MB

07 MultiIndex/096 Create a MultiIndex with the set_index() Method.mp421.05MB

07 MultiIndex/097 The .get_level_values() Method.mp416.54MB

07 MultiIndex/098 The .set_names() Method.mp46.09MB

07 MultiIndex/099 The sort_index() Method.mp410.25MB

07 MultiIndex/100 Extract Rows from a MultiIndex DataFrame.mp417.34MB

07 MultiIndex/101 The .transpose() Method and MultiIndex on Column Level.mp411.92MB

07 MultiIndex/102 The .swaplevel() Method.mp45.18MB

07 MultiIndex/103 The .stack() Method.mp413.19MB

07 MultiIndex/104 The .unstack() Method Part 1.mp48.48MB

07 MultiIndex/105 The .unstack() Method Part 2.mp414.53MB

07 MultiIndex/106 The .unstack() Method Part 3.mp411.96MB

07 MultiIndex/107 The .pivot() Method.mp412.11MB

07 MultiIndex/108 The .pivot_table() Method.mp422.16MB

07 MultiIndex/109 The pd.melt() Method.mp417.26MB

08 GroupBy/110 Intro to the Groupby Module.mp414.29MB

08 GroupBy/111 First Operations with groupby Object.mp423.08MB

08 GroupBy/112 Retrieve A Group with the .get_group() Method.mp410.14MB

08 GroupBy/113 Methods on the Groupby Object and DataFrame Columns.mp420.49MB

08 GroupBy/114 Grouping by Multiple Columns.mp410.34MB

08 GroupBy/115 The .agg() Method.mp413.18MB

08 GroupBy/116 Iterating through Groups.mp421.37MB

09 Merging Joining and Concatenating/117 Intro to the Merging Joining and Concatenating Module.mp411.46MB

09 Merging Joining and Concatenating/118 The pd.concat() Method Part 1.mp412.56MB

09 Merging Joining and Concatenating/119 The pd.concat() Method Part 2.mp413.2MB

09 Merging Joining and Concatenating/120 The .append() Method on a DataFrame.mp45.13MB

09 Merging Joining and Concatenating/121 Inner Joins Part 1.mp417.92MB

09 Merging Joining and Concatenating/122 Inner Joins Part 2.mp417.75MB

09 Merging Joining and Concatenating/123 Outer Joins.mp425.94MB

09 Merging Joining and Concatenating/124 Left Joins.mp420.99MB

09 Merging Joining and Concatenating/125 The left_on and right_on Parameters.mp420.24MB

09 Merging Joining and Concatenating/126 Merging by Indexes with the left_index and right_index Parameters.mp422.7MB

09 Merging Joining and Concatenating/127 The .join() Method.mp46.27MB

09 Merging Joining and Concatenating/128 The pd.merge() Method.mp46.84MB

10 Working with Dates and Times/129 Intro to the Working with Dates and Times Module.mp46.32MB

10 Working with Dates and Times/130 Review of Pythons datetime Module.mp416.73MB

10 Working with Dates and Times/131 The pandas Timestamp Object.mp412.8MB

10 Working with Dates and Times/132 The pandas DateTimeIndex Object.mp49.65MB

10 Working with Dates and Times/133 The pd.to_datetime() Method.mp422.88MB

10 Working with Dates and Times/134 Create Range of Dates with the pd.date_range() Method Part 1.mp419.68MB

10 Working with Dates and Times/135 Create Range of Dates with the pd.date_range() Method Part 2.mp418.54MB

10 Working with Dates and Times/136 Create Range of Dates with the pd.date_range() Method Part 3.mp416.33MB

10 Working with Dates and Times/137 The .dt Accessor.mp413.68MB

10 Working with Dates and Times/138 Install pandas-datareader Library.mp45.9MB

10 Working with Dates and Times/139 Import Financial Data Set with pandas_datareader Library.mp425.48MB

10 Working with Dates and Times/140 Selecting Rows from a DataFrame with a DateTimeIndex.mp418.34MB

10 Working with Dates and Times/141 Timestamp Object Attributes.mp419.57MB

10 Working with Dates and Times/142 The .truncate() Method.mp49.04MB

10 Working with Dates and Times/143 pd.DateOffset Objects.mp425.58MB

10 Working with Dates and Times/144 More Fun with pd.DateOffset Objects.mp431.91MB

10 Working with Dates and Times/145 The pandas Timedelta Object.mp415.41MB

10 Working with Dates and Times/146 Timedeltas in a Dataset.mp419.55MB

11 Panels/147 Intro to the Module Fetch Panel Dataset from Google Finance.mp413.67MB

11 Panels/148 The Axes of a Panel Object.mp416.31MB

11 Panels/149 Panel Attributes.mp410.49MB

11 Panels/150 Use Bracket Notation to Extract a DataFrame from a Panel.mp48.25MB

11 Panels/151 Extracting with the .loc .iloc and .ix Methods.mp413.53MB

11 Panels/152 Convert Panel to a MultiIndex DataFrame (and Vice Versa).mp48.68MB

11 Panels/153 The .major_xs() Method.mp412.11MB

11 Panels/154 The .minor_xs() Method.mp413.62MB

11 Panels/155 Transpose a Panel with the .transpose() Method.mp415.73MB

11 Panels/156 The .swapaxes() Method.mp49.71MB

12 Input and Output/157 Intro to the Input and Output Module.mp42.8MB

12 Input and Output/158 Feed pd.read_csv() Method a URL Argument.mp47.6MB

12 Input and Output/159 Quick Object Conversions.mp411.35MB

12 Input and Output/160 Export DataFrame to CSV File with the .to_csv() Method.mp411.36MB

12 Input and Output/161 Install xlrd and openpyxl Libraries to Read and Write Excel Files.mp45.98MB

12 Input and Output/162 Import Excel File into pandas.mp419.13MB

12 Input and Output/163 Export Excel File.mp417.8MB

13 Visualization/164 Intro to Visualization Module.mp47.31MB

13 Visualization/165 The .plot() Method.mp418.97MB

13 Visualization/166 Modifying Aesthetics with Templates.mp412.08MB

13 Visualization/167 Bar Graphs.mp412.27MB

13 Visualization/168 Pie Charts.mp49.87MB

13 Visualization/169 Histograms.mp412.16MB

14 Options and Settings/170 Introduction to the Options and Settings Module.mp43.32MB

14 Options and Settings/171 Changing pandas Options with Attributes and Dot Syntax.mp419.83MB

14 Options and Settings/172 Changing pandas Options with Methods.mp413.92MB

14 Options and Settings/173 The precision Option.mp46.1MB

15 Conclusion/174 Conclusion.mp42.95MB