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Pandas Tutorial

This repository makes use of most of the basic functions of pandas library. Nothing Fancy.

What does it do?

  • Can analyse 1082 indicators for almost all of the countries in existance. For eg
In [21]: temp_df.sample(10)
Out[21]:
01964Tariffrate, applied, weightedmean, manufactu... TM_TAX_MANF_WM_AR_ZS816Primaryeducation, duration (years) SE_PRM_DURS68Averageprecipitationindepth (mmperyear) AG_LND_PRCP_MM634Mortalityrate, adult, male (per1,000malead... SP_DYN_AMRT_MA584Manufacturing, valueadded (constant2000US$) NV_IND_MANF_KD840Pumppriceforgasoline (US$ perliter) EP_PMP_SGAS_CD254Employees, services, male (%ofmaleemployment) SL_SRV_EMPL_MA_ZS920Secondaryeducation, vocationalpupils (%female) SE_SEC_ENRL_VO_FE_ZS733Nitrousoxideemissionsinindustrialandener... EN_ATM_NOXE_EI_ZS100Chemicals (%ofvalueaddedinmanufacturing) NV_MNF_CHEM_ZS_UN
In [22]: len(indicators)
Out[22]: 1082
  • To narrow down the datatset and the declutter the graph, only asian countries have been analysed for the parameter(indicators).
In [23]: df_temp = pd.read_html('Country_Codes2.html')
...: df = df_temp[0]
...: a_c_codes = [df['alpha-3'][i] foriin range(len(df['country-code'])) if df['region'][i] == 'Asia' ]
...: print(len(a_c_codes))
Out[23] : 51
  • Built the DataFrame from the paramters for various countries and Pickle it for further use to save time
  • Calculate Percentage Change in the data from the previous value.
  • Calculate Percentage Change in the data from the starting (initial) value.
  • Plot both of the above DataFrames.
  • Pickle both the DataFrames for quick access.
  • Plot the correlations between different countries
In [24]: growth_correlations.describe()['IND']
Out[24]:
count48.000000mean0.900618std0.079899min0.71069225%0.86852250%0.92883675%0.957534max1.000000Name: IND, dtype: float64
In [25]: growth_correlations['IND'].head()
Out[25]:
AFG0.726658ARM0.953290AZE0.932335BHR0.956719BGD0.767517Name: IND, dtype: float64
  • Resample the data for every 2 years.
  • Observe the Standard Deviation between rolling values of every 2 years wrt to the original data. alt text
  • Compared Correlation of India-Japan and India-China for the selected Parameter alt text

Dependencies

  • [Pandas] - Bummer !
  • [Numpy] - For using arrays in Python
  • [Quandl] - For building Data-Sets
  • [Pickle] - For quick reuse of the DataFrame later
  • [Matplotlib] - For plotting the graphs

Thanks for your Time, Consider it giving a star?

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Pandas Tutorial
This repository makes use of most of the basic functions of pandas library.
Nothing Fancy.
What does it do?
  • Can analyse 1082 indicators for almost all of the countries in existance.
For eg
In [21]: temp_df.sample(10)
Out[21]:
01964Tariffrate, applied, weightedmean, manufactu... TM_TAX_MANF_WM_AR_ZS816Primaryeducation, duration (years) SE_PRM_DURS68Averageprecipitationindepth (mmperyear) AG_LND_PRCP_MM634Mortalityrate, adult, male (per1,000malead... SP_DYN_AMRT_MA584Manufacturing, valueadded (constant2000US$) NV_IND_MANF_KD840Pumppriceforgasoline (US$ perliter) EP_PMP_SGAS_CD254Employees, services, male (%ofmaleemployment) SL_SRV_EMPL_MA_ZS920Secondaryeducation, vocationalpupils (%female) SE_SEC_ENRL_VO_FE_ZS733Nitrousoxideemissionsinindustrialandener... EN_ATM_NOXE_EI_ZS100Chemicals (%ofvalueaddedinmanufacturing) NV_MNF_CHEM_ZS_UN
In [22]: len(indicators)
Out[22]: 1082
  • To narrow down the datatset and the declutter the graph, only asian countries have been analysed for the parameter(indicators).
In [23]: df_temp = pd.read_html('Country_Codes2.html')
...: df = df_temp[0]
...: a_c_codes = [df['alpha-3'][i] foriin range(len(df['country-code'])) if df['region'][i] == 'Asia' ]
...: print(len(a_c_codes))
Out[23] : 51
  • Built the DataFrame from the paramters for various countries and Pickle it for further use to save time
  • Calculate Percentage Change in the data from the previous value.
  • Calculate Percentage Change in the data from the starting (initial) value.
  • Plot both of the above DataFrames.
  • Pickle both the DataFrames for quick access.
  • Plot the correlations between different countries
In [24]: growth_correlations.describe()['IND']
Out[24]:
count48.000000mean0.900618std0.079899min0.71069225%0.86852250%0.92883675%0.957534max1.000000Name: IND, dtype: float64
In [25]: growth_correlations['IND'].head()
Out[25]:
AFG0.726658ARM0.953290AZE0.932335BHR0.956719BGD0.767517Name: IND, dtype: float64
  • Resample the data for every 2 years.
  • Observe the Standard Deviation between rolling values of every 2 years wrt to the original data.
alt text
  • Compared Correlation of India-Japan and India-China for the selected Parameter
alt text
Dependencies
  • [Pandas] - Bummer !
  • [Numpy] - For using arrays in Python
  • [Quandl] - For building Data-Sets
  • [Pickle] - For quick reuse of the DataFrame later
  • [Matplotlib] - For plotting the graphs
Thanks for your Time, Consider it giving a star?
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Pandas Tutorial
This repository makes use of most of the basic functions of pandas library.
Nothing Fancy.
What does it do?
  • Can analyse 1082 indicators for almost all of the countries in existance.
For eg
In [21]: temp_df.sample(10)
Out[21]:
01964Tariffrate, applied, weightedmean, manufactu... TM_TAX_MANF_WM_AR_ZS816Primaryeducation, duration (years) SE_PRM_DURS68Averageprecipitationindepth (mmperyear) AG_LND_PRCP_MM634Mortalityrate, adult, male (per1,000malead... SP_DYN_AMRT_MA584Manufacturing, valueadded (constant2000US$) NV_IND_MANF_KD840Pumppriceforgasoline (US$ perliter) EP_PMP_SGAS_CD254Employees, services, male (%ofmaleemployment) SL_SRV_EMPL_MA_ZS920Secondaryeducation, vocationalpupils (%female) SE_SEC_ENRL_VO_FE_ZS733Nitrousoxideemissionsinindustrialandener... EN_ATM_NOXE_EI_ZS100Chemicals (%ofvalueaddedinmanufacturing) NV_MNF_CHEM_ZS_UN
In [22]: len(indicators)
Out[22]: 1082
  • To narrow down the datatset and the declutter the graph, only asian countries have been analysed for the parameter(indicators).
In [23]: df_temp = pd.read_html('Country_Codes2.html')
...: df = df_temp[0]
...: a_c_codes = [df['alpha-3'][i] foriin range(len(df['country-code'])) if df['region'][i] == 'Asia' ]
...: print(len(a_c_codes))
Out[23] : 51
  • Built the DataFrame from the paramters for various countries and Pickle it for further use to save time
  • Calculate Percentage Change in the data from the previous value.
  • Calculate Percentage Change in the data from the starting (initial) value.
  • Plot both of the above DataFrames.
  • Pickle both the DataFrames for quick access.
  • Plot the correlations between different countries
In [24]: growth_correlations.describe()['IND']
Out[24]:
count48.000000mean0.900618std0.079899min0.71069225%0.86852250%0.92883675%0.957534max1.000000Name: IND, dtype: float64
In [25]: growth_correlations['IND'].head()
Out[25]:
AFG0.726658ARM0.953290AZE0.932335BHR0.956719BGD0.767517Name: IND, dtype: float64
  • Resample the data for every 2 years.
  • Observe the Standard Deviation between rolling values of every 2 years wrt to the original data.
alt text
  • Compared Correlation of India-Japan and India-China for the selected Parameter
alt text
Dependencies
  • [Pandas] - Bummer !
  • [Numpy] - For using arrays in Python
  • [Quandl] - For building Data-Sets
  • [Pickle] - For quick reuse of the DataFrame later
  • [Matplotlib] - For plotting the graphs
Thanks for your Time, Consider it giving a star?
About
Illustrates use of basic Funtions inside the Pandas Library
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Pandas Tutorial
This repository makes use of most of the basic functions of pandas library.
Nothing Fancy.
What does it do?
  • Can analyse 1082 indicators for almost all of the countries in existance.
For eg
In [21]: temp_df.sample(10)
Out[21]:
01964Tariffrate, applied, weightedmean, manufactu... TM_TAX_MANF_WM_AR_ZS816Primaryeducation, duration (years) SE_PRM_DURS68Averageprecipitationindepth (mmperyear) AG_LND_PRCP_MM634Mortalityrate, adult, male (per1,000malead... SP_DYN_AMRT_MA584Manufacturing, valueadded (constant2000US$) NV_IND_MANF_KD840Pumppriceforgasoline (US$ perliter) EP_PMP_SGAS_CD254Employees, services, male (%ofmaleemployment) SL_SRV_EMPL_MA_ZS920Secondaryeducation, vocationalpupils (%female) SE_SEC_ENRL_VO_FE_ZS733Nitrousoxideemissionsinindustrialandener... EN_ATM_NOXE_EI_ZS100Chemicals (%ofvalueaddedinmanufacturing) NV_MNF_CHEM_ZS_UN
In [22]: len(indicators)
Out[22]: 1082
  • To narrow down the datatset and the declutter the graph, only asian countries have been analysed for the parameter(indicators).
In [23]: df_temp = pd.read_html('Country_Codes2.html')
...: df = df_temp[0]
...: a_c_codes = [df['alpha-3'][i] foriin range(len(df['country-code'])) if df['region'][i] == 'Asia' ]
...: print(len(a_c_codes))
Out[23] : 51
  • Built the DataFrame from the paramters for various countries and Pickle it for further use to save time
  • Calculate Percentage Change in the data from the previous value.
  • Calculate Percentage Change in the data from the starting (initial) value.
  • Plot both of the above DataFrames.
  • Pickle both the DataFrames for quick access.
  • Plot the correlations between different countries
In [24]: growth_correlations.describe()['IND']
Out[24]:
count48.000000mean0.900618std0.079899min0.71069225%0.86852250%0.92883675%0.957534max1.000000Name: IND, dtype: float64
In [25]: growth_correlations['IND'].head()
Out[25]:
AFG0.726658ARM0.953290AZE0.932335BHR0.956719BGD0.767517Name: IND, dtype: float64
  • Resample the data for every 2 years.
  • Observe the Standard Deviation between rolling values of every 2 years wrt to the original data.
alt text
  • Compared Correlation of India-Japan and India-China for the selected Parameter
alt text
Dependencies
  • [Pandas] - Bummer !
  • [Numpy] - For using arrays in Python
  • [Quandl] - For building Data-Sets
  • [Pickle] - For quick reuse of the DataFrame later
  • [Matplotlib] - For plotting the graphs
Thanks for your Time, Consider it giving a star?
About
Illustrates use of basic Funtions inside the Pandas Library
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Pandas Tutorial
This repository makes use of most of the basic functions of pandas library.
Nothing Fancy.
What does it do?
  • Can analyse 1082 indicators for almost all of the countries in existance.
For eg
In [21]: temp_df.sample(10)
Out[21]:
01964Tariffrate, applied, weightedmean, manufactu... TM_TAX_MANF_WM_AR_ZS816Primaryeducation, duration (years) SE_PRM_DURS68Averageprecipitationindepth (mmperyear) AG_LND_PRCP_MM634Mortalityrate, adult, male (per1,000malead... SP_DYN_AMRT_MA584Manufacturing, valueadded (constant2000US$) NV_IND_MANF_KD840Pumppriceforgasoline (US$ perliter) EP_PMP_SGAS_CD254Employees, services, male (%ofmaleemployment) SL_SRV_EMPL_MA_ZS920Secondaryeducation, vocationalpupils (%female) SE_SEC_ENRL_VO_FE_ZS733Nitrousoxideemissionsinindustrialandener... EN_ATM_NOXE_EI_ZS100Chemicals (%ofvalueaddedinmanufacturing) NV_MNF_CHEM_ZS_UN
In [22]: len(indicators)
Out[22]: 1082
  • To narrow down the datatset and the declutter the graph, only asian countries have been analysed for the parameter(indicators).
In [23]: df_temp = pd.read_html('Country_Codes2.html')
...: df = df_temp[0]
...: a_c_codes = [df['alpha-3'][i] foriin range(len(df['country-code'])) if df['region'][i] == 'Asia' ]
...: print(len(a_c_codes))
Out[23] : 51
  • Built the DataFrame from the paramters for various countries and Pickle it for further use to save time
  • Calculate Percentage Change in the data from the previous value.
  • Calculate Percentage Change in the data from the starting (initial) value.
  • Plot both of the above DataFrames.
  • Pickle both the DataFrames for quick access.
  • Plot the correlations between different countries
In [24]: growth_correlations.describe()['IND']
Out[24]:
count48.000000mean0.900618std0.079899min0.71069225%0.86852250%0.92883675%0.957534max1.000000Name: IND, dtype: float64
In [25]: growth_correlations['IND'].head()
Out[25]:
AFG0.726658ARM0.953290AZE0.932335BHR0.956719BGD0.767517Name: IND, dtype: float64
  • Resample the data for every 2 years.
  • Observe the Standard Deviation between rolling values of every 2 years wrt to the original data.
alt text
  • Compared Correlation of India-Japan and India-China for the selected Parameter
alt text
Dependencies
  • [Pandas] - Bummer !
  • [Numpy] - For using arrays in Python
  • [Quandl] - For building Data-Sets
  • [Pickle] - For quick reuse of the DataFrame later
  • [Matplotlib] - For plotting the graphs
Thanks for your Time, Consider it giving a star?
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Illustrates use of basic Funtions inside the Pandas Library
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Pandas Tutorial
This repository makes use of most of the basic functions of pandas library.
Nothing Fancy.
What does it do?
  • Can analyse 1082 indicators for almost all of the countries in existance.
For eg
In [21]: temp_df.sample(10)
Out[21]:
01964Tariffrate, applied, weightedmean, manufactu... TM_TAX_MANF_WM_AR_ZS816Primaryeducation, duration (years) SE_PRM_DURS68Averageprecipitationindepth (mmperyear) AG_LND_PRCP_MM634Mortalityrate, adult, male (per1,000malead... SP_DYN_AMRT_MA584Manufacturing, valueadded (constant2000US$) NV_IND_MANF_KD840Pumppriceforgasoline (US$ perliter) EP_PMP_SGAS_CD254Employees, services, male (%ofmaleemployment) SL_SRV_EMPL_MA_ZS920Secondaryeducation, vocationalpupils (%female) SE_SEC_ENRL_VO_FE_ZS733Nitrousoxideemissionsinindustrialandener... EN_ATM_NOXE_EI_ZS100Chemicals (%ofvalueaddedinmanufacturing) NV_MNF_CHEM_ZS_UN
In [22]: len(indicators)
Out[22]: 1082
  • To narrow down the datatset and the declutter the graph, only asian countries have been analysed for the parameter(indicators).
In [23]: df_temp = pd.read_html('Country_Codes2.html')
...: df = df_temp[0]
...: a_c_codes = [df['alpha-3'][i] foriin range(len(df['country-code'])) if df['region'][i] == 'Asia' ]
...: print(len(a_c_codes))
Out[23] : 51
  • Built the DataFrame from the paramters for various countries and Pickle it for further use to save time
  • Calculate Percentage Change in the data from the previous value.
  • Calculate Percentage Change in the data from the starting (initial) value.
  • Plot both of the above DataFrames.
  • Pickle both the DataFrames for quick access.
  • Plot the correlations between different countries
In [24]: growth_correlations.describe()['IND']
Out[24]:
count48.000000mean0.900618std0.079899min0.71069225%0.86852250%0.92883675%0.957534max1.000000Name: IND, dtype: float64
In [25]: growth_correlations['IND'].head()
Out[25]:
AFG0.726658ARM0.953290AZE0.932335BHR0.956719BGD0.767517Name: IND, dtype: float64
  • Resample the data for every 2 years.
  • Observe the Standard Deviation between rolling values of every 2 years wrt to the original data.
alt text
  • Compared Correlation of India-Japan and India-China for the selected Parameter
alt text
Dependencies
  • [Pandas] - Bummer !
  • [Numpy] - For using arrays in Python
  • [Quandl] - For building Data-Sets
  • [Pickle] - For quick reuse of the DataFrame later
  • [Matplotlib] - For plotting the graphs
Thanks for your Time, Consider it giving a star?
About
Illustrates use of basic Funtions inside the Pandas Library
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Pandas Tutorial
This repository makes use of most of the basic functions of pandas library.
Nothing Fancy.
What does it do?
  • Can analyse 1082 indicators for almost all of the countries in existance.
For eg
In [21]: temp_df.sample(10)
Out[21]:
01964Tariffrate, applied, weightedmean, manufactu... TM_TAX_MANF_WM_AR_ZS816Primaryeducation, duration (years) SE_PRM_DURS68Averageprecipitationindepth (mmperyear) AG_LND_PRCP_MM634Mortalityrate, adult, male (per1,000malead... SP_DYN_AMRT_MA584Manufacturing, valueadded (constant2000US$) NV_IND_MANF_KD840Pumppriceforgasoline (US$ perliter) EP_PMP_SGAS_CD254Employees, services, male (%ofmaleemployment) SL_SRV_EMPL_MA_ZS920Secondaryeducation, vocationalpupils (%female) SE_SEC_ENRL_VO_FE_ZS733Nitrousoxideemissionsinindustrialandener... EN_ATM_NOXE_EI_ZS100Chemicals (%ofvalueaddedinmanufacturing) NV_MNF_CHEM_ZS_UN
In [22]: len(indicators)
Out[22]: 1082
  • To narrow down the datatset and the declutter the graph, only asian countries have been analysed for the parameter(indicators).
In [23]: df_temp = pd.read_html('Country_Codes2.html')
...: df = df_temp[0]
...: a_c_codes = [df['alpha-3'][i] foriin range(len(df['country-code'])) if df['region'][i] == 'Asia' ]
...: print(len(a_c_codes))
Out[23] : 51
  • Built the DataFrame from the paramters for various countries and Pickle it for further use to save time
  • Calculate Percentage Change in the data from the previous value.
  • Calculate Percentage Change in the data from the starting (initial) value.
  • Plot both of the above DataFrames.
  • Pickle both the DataFrames for quick access.
  • Plot the correlations between different countries
In [24]: growth_correlations.describe()['IND']
Out[24]:
count48.000000mean0.900618std0.079899min0.71069225%0.86852250%0.92883675%0.957534max1.000000Name: IND, dtype: float64
In [25]: growth_correlations['IND'].head()
Out[25]:
AFG0.726658ARM0.953290AZE0.932335BHR0.956719BGD0.767517Name: IND, dtype: float64
  • Resample the data for every 2 years.
  • Observe the Standard Deviation between rolling values of every 2 years wrt to the original data.
alt text
  • Compared Correlation of India-Japan and India-China for the selected Parameter
alt text
Dependencies
  • [Pandas] - Bummer !
  • [Numpy] - For using arrays in Python
  • [Quandl] - For building Data-Sets
  • [Pickle] - For quick reuse of the DataFrame later
  • [Matplotlib] - For plotting the graphs
Thanks for your Time, Consider it giving a star?
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Illustrates use of basic Funtions inside the Pandas Library
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Skip to content
BranchesTags
Repository files navigation
Pandas Tutorial
This repository makes use of most of the basic functions of pandas library.
Nothing Fancy.
What does it do?
  • Can analyse 1082 indicators for almost all of the countries in existance.
For eg
In [21]: temp_df.sample(10)
Out[21]:
01964Tariffrate, applied, weightedmean, manufactu... TM_TAX_MANF_WM_AR_ZS816Primaryeducation, duration (years) SE_PRM_DURS68Averageprecipitationindepth (mmperyear) AG_LND_PRCP_MM634Mortalityrate, adult, male (per1,000malead... SP_DYN_AMRT_MA584Manufacturing, valueadded (constant2000US$) NV_IND_MANF_KD840Pumppriceforgasoline (US$ perliter) EP_PMP_SGAS_CD254Employees, services, male (%ofmaleemployment) SL_SRV_EMPL_MA_ZS920Secondaryeducation, vocationalpupils (%female) SE_SEC_ENRL_VO_FE_ZS733Nitrousoxideemissionsinindustrialandener... EN_ATM_NOXE_EI_ZS100Chemicals (%ofvalueaddedinmanufacturing) NV_MNF_CHEM_ZS_UN
In [22]: len(indicators)
Out[22]: 1082
  • To narrow down the datatset and the declutter the graph, only asian countries have been analysed for the parameter(indicators).
In [23]: df_temp = pd.read_html('Country_Codes2.html')
...: df = df_temp[0]
...: a_c_codes = [df['alpha-3'][i] foriin range(len(df['country-code'])) if df['region'][i] == 'Asia' ]
...: print(len(a_c_codes))
Out[23] : 51
  • Built the DataFrame from the paramters for various countries and Pickle it for further use to save time
  • Calculate Percentage Change in the data from the previous value.
  • Calculate Percentage Change in the data from the starting (initial) value.
  • Plot both of the above DataFrames.
  • Pickle both the DataFrames for quick access.
  • Plot the correlations between different countries
In [24]: growth_correlations.describe()['IND']
Out[24]:
count48.000000mean0.900618std0.079899min0.71069225%0.86852250%0.92883675%0.957534max1.000000Name: IND, dtype: float64
In [25]: growth_correlations['IND'].head()
Out[25]:
AFG0.726658ARM0.953290AZE0.932335BHR0.956719BGD0.767517Name: IND, dtype: float64
  • Resample the data for every 2 years.
  • Observe the Standard Deviation between rolling values of every 2 years wrt to the original data.
alt text
  • Compared Correlation of India-Japan and India-China for the selected Parameter
alt text
Dependencies
  • [Pandas] - Bummer !
  • [Numpy] - For using arrays in Python
  • [Quandl] - For building Data-Sets
  • [Pickle] - For quick reuse of the DataFrame later
  • [Matplotlib] - For plotting the graphs
Thanks for your Time, Consider it giving a star?
About
Illustrates use of basic Funtions inside the Pandas Library
Topics
pandas-tutorial
Resources
Readme
Activity
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages